Data Pipelines: ETL vs ELT¶

Data pipeline is a generic term for moving data from one place to another. For example, it could be moving data from one server to another server.

ETL¶

An ETL pipeline is a specific kind of data pipeline and very common. ETL stands for Extract, Transform, Load. Imagine that you have a database containing web log data. Each entry contains the IP address of a user, a timestamp, and the link that the user clicked.

What if your company wanted to run an analysis of links clicked by city and by day? You would need another data set that maps IP address to a city, and you would also need to extract the day from the timestamp. With an ETL pipeline, you could run code once per day that would extract the previous day's log data, map IP address to city, aggregate link clicks by city, and then load these results into a new database. That way, a data analyst or scientist would have access to a table of log data by city and day. That is more convenient than always having to run the same complex data transformations on the raw web log data.

Before cloud computing, businesses stored their data on large, expensive, private servers. Running queries on large data sets, like raw web log data, could be expensive both economically and in terms of time. But data analysts might need to query a database multiple times even in the same day; hence, pre-aggregating the data with an ETL pipeline makes sense.

ELT¶

ELT (extract, load, transform) pipelines have gained traction since the advent of cloud computing. Cloud computing has lowered the cost of storing data and running queries on large, raw data sets. Many of these cloud services, like Amazon Redshift, Google BigQuery, or IBM Db2 can be queried using SQL or a SQL-like language. With these tools, the data gets extracted, then loaded directly, and finally transformed at the end of the pipeline.

However, ETL pipelines are still used even with these cloud tools. Oftentimes, it still makes sense to run ETL pipelines and store data in a more readable or intuitive format. This can help data analysts and scientists work more efficiently as well as help an organization become more data driven.

Outline of the Lesson¶

Extract data from different sources such as:
- csv files
- json files
- APIs
Transform data
- combining data from different sources
- data cleaning
- data types
- parsing dates
- file encodings
- missing data
- duplicate data
- dummy variables
- remove outliers
- scaling features
- engineering features
Load
- send the transformed data to a database
ETL Pipeline
- code an ETL pipeline

This lesson contains many Jupyter notebook exercises where you can practice the different parts of an ETL pipeline. Some of the exercises are challenging, but they also contain hints to help you get through them. You'll notice that the "transformation" section is relatively long. You'll oftentimes hear data scientists say that cleaning and transforming data is how they spend a majority of their time. This lesson reflects that reality.

World Bank Data¶

In the next section, you'll find a series of exercises. These are relatively brief and focus on extracting, or in other words, reading in data from different sources. The goal is to familiarize yourself with different types of files and see how the same data can be formatted in different ways. This lesson assumes you have experience with pandas and basic programming skills.

This lesson uses data from the World Bank. The data comes from two sources:

World Bank Indicator Data - This data contains socio-economic indicators for countries around the world. A few example indicators include population, arable land, and central government debt.
World Bank Project Data - This data set contains information about World Bank project lending since 1947.

How to Tackle the Exercises¶

This course assumes you have experience manipulating data with the Pandas library, which is covered in the data analyst nanodegree. Some of these transformation exercises are challenging. The most challenging exercises are marked (challenging). If an exercise is marked as a challenge, it means you’ll get something out of solving it, but it’s not essential for understanding the lesson material or for getting through the final project at the end of this data engineering course.

Throughout the exercises, you might have to read the pandas documentation or search outside the classroom for how to do a certain processing technique. That is not just expected but also encouraged. As a data scientist professional, you will oftentimes have to research how to do something on your own much like what software engineers do. See this answer on Quora about how often do people use stackoverflow when working on data science projects?.

Use Google and other search engines when you're not sure how to do something!

What You Will do in the Next Section¶

In the next section of the lesson, you'll learn about the extract portion of an ETL pipeline. You’ll get practice with a series of exercises. These exercises are relatively brief and focus on extracting, or in other words, reading in data from different sources. The goal is to familiarize yourself with different types of files and see how the same data can be formatted in different ways.

For a review of pandas, click on the "Extracurricular" section of the classroom. Open the Prerequisite: Python for Data Analysis course, and go to Lesson 7: Pandas.

Summary of the data file types you'll work with¶

CSV files¶

CSV stands for comma-separated values. These types of files separate values with a comma, and each entry is on a separate line. Oftentimes, the first entry will contain variable names. Here is an example of what CSV data looks like. This is an abbreviated version of the first three lines in the World Bank projects data csv file.

JSON¶

JSON is a file format with key/value pairs. It looks like a Python dictionary. The exact same CSV file represented in JSON could look like this:

XML¶

Another data format is called XML (Extensible Markup Language). XML is very similar to HTML at least in terms of formatting. The main difference between the two is that HTML has pre-defined tags that are standardized. In XML, tags can be tailored to the data set. Here is what this same data would look like as XML. XML is falling out of favor especially because JSON tends to be easier to navigate; however, you still might come across XML data. The World Bank API, for example, can return either XML data or JSON data. From a data perspective, the process for handling HTML and XML data is essentially the same.

SQL databases¶

SQL databases store data in tables using primary and foreign keys. In a SQL database, the same data would look like this:

Text Files¶

This course won't go into much detail about text data. There are other Udacity courses, namely on natural language processing, that go into the details of processing text for machine learning.

Extracting Data from the Web¶

In this lesson, you'll see how to extract data from the web using an APIs (Application Programming Interface). APIs generally provide data in either JSON or XML format.

Companies and organizations provide APIs so that programmers can access data in an official, safe way. APIs allow you to download, and sometimes even upload or modify, data from a web server without giving you direct access.

Goal of the ETL Lesson¶

The main goal of this ETL pipelines lesson is to take the World Bank Project data set and merge this data with the World Bank indicator data. Then you'll load the merged data into a database.

In the process, you'll need to transform these data sets in different ways. And finally, you'll code an ETL pipeline to extract, transform, and load the data all in one step.

Extracting data from a csv file¶

The first step in an ETL pipeline is extraction. Data comes in all types of different formats, and you'll practice extracting data from csv files, JSON files, XML files, SQL databases, and the web.

In this first exercise, you'll practice extracting data from a CSV file and then navigating through the results. You'll see that extracting data is not always a straight-forward process.

This exercise contains a series of coding questions for you to solve. If you get stuck, there is a solution file called 1_csv_exercise_solution.ipynb. You can find this solution file by going to File->Open and then clicking on the file name.

Part 1 projects_data.csv¶

You'll be using the following csv files:

projects_data.csv
population_data.csv

As a first step, try importing the projects data using the pandas read_csv method. The file path is just '../data/projects_data.csv'. You can see the file if you click on File->Open in the workspace and open the data folder.

# TODO: import the projects_data.csv file using the pandas library
# Store the results in the df_projects variable
import pandas as pd
df_projects = pd.read_csv('projects_data.csv')

/Users/xuhao3/opt/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3063: DtypeWarning: Columns (44) have mixed types.Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)

Did you get a DType warning? Read about what this warning is in the pandas documentation.

Pandas tries to figure out programatically the data type of each column (integer, float, boolean, string). In this case, pandas could not automatically figure out the data type. That is because some columns have more than one possible data types. In other words, this data is messy.

You can use the dtype option to specify the data type of each column. Because there are so many columns in this data set, you can set all columns to be strings at least for now.

Try reading in the data set again using the read_csv() method. This time, also use the option dtype=str so that pandas treats everything like a string.

# TODO: Read in the projects_data.csv file using the read_csv method 
# and dtype = str option
df_projects = pd.read_csv('projects_data.csv', dtype = str)

# Run the cell below to see what the data looks like
df_projects.head()

# TODO: count the number of null values in the data set
# HINT: use the isnull() and sum() methods
df_projects.isnull().sum()

id                              0
regionname                      0
countryname                     0
prodline                        0
lendinginstr                  246
lendinginstrtype              246
envassesmentcategorycode     5811
supplementprojectflg           53
productlinetype                 0
projectstatusdisplay            4
status                          4
project_name                    0
boardapprovaldate            1504
board_approval_month         1504
closingdate                  3349
lendprojectcost               125
ibrdcommamt                     0
idacommamt                      0
totalamt                        0
grantamt                        0
borrower                     5919
impagency                    6097
url                             0
projectdoc                  18248
majorsector_percent         18248
sector1                         0
sector2                      8721
sector3                     11761
sector4                     13872
sector5                     15535
sector                        638
mjsector1                   18248
mjsector2                   18248
mjsector3                   18248
mjsector4                   18248
mjsector5                   18248
mjsector                      638
theme1                          0
theme2                       8395
theme3                      10764
theme4                      13014
theme5                      15320
theme                       18248
goal                         6115
financier                   11188
mjtheme1name                18248
mjtheme2name                18248
mjtheme3name                18248
mjtheme4name                18248
mjtheme5name                18248
location                    13922
GeoLocID                    13922
GeoLocName                  13923
Latitude                    13922
Longitude                   13922
Country                     14045
Unnamed: 56                 18248
dtype: int64

Notice that the number 18248 shows up multiple times. There is also a countryname column with 0 missing values and a Country column with 14045 missing values. This data set clearly has some issues that will need to be solved in the transform part of the pipeline.

Next, output the shape of the data frame

# TODO: output the shape of the data frame
df_projects.shape

(18248, 57)

There are 18248 rows in this data set. Considering many columns had 18248 NaN values, many columns in the data set are filled completely with NaN values.

Part 2 population_data.csv¶

Next, use the pandas read_csv method to read in the population_data.csv file. The path to this file is "../data/population_data.csv". When you try to read in this data set using pandas, you'll get an error because there is something wrong with the data.

# TODO: read in the population_data.csv file using the read_csv() method
# Put the results in a variable called df_population
df_population = pd.read_csv('population_data.csv')

---------------------------------------------------------------------------
ParserError                               Traceback (most recent call last)
<ipython-input-11-a93042ce333b> in <module>
      1 # TODO: read in the population_data.csv file using the read_csv() method
      2 # Put the results in a variable called df_population
----> 3 df_population = pd.read_csv('population_data.csv')

~/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
    674         )
    675 
--> 676         return _read(filepath_or_buffer, kwds)
    677 
    678     parser_f.__name__ = name

~/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
    452 
    453     try:
--> 454         data = parser.read(nrows)
    455     finally:
    456         parser.close()

~/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
   1131     def read(self, nrows=None):
   1132         nrows = _validate_integer("nrows", nrows)
-> 1133         ret = self._engine.read(nrows)
   1134 
   1135         # May alter columns / col_dict

~/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in read(self, nrows)
   2035     def read(self, nrows=None):
   2036         try:
-> 2037             data = self._reader.read(nrows)
   2038         except StopIteration:
   2039             if self._first_chunk:

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.read()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_low_memory()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._read_rows()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._tokenize_rows()

pandas/_libs/parsers.pyx in pandas._libs.parsers.raise_parser_error()

ParserError: Error tokenizing data. C error: Expected 3 fields in line 5, saw 63

There is something wrong with this data set. You should see an error that says "expected 3 fields in line 5, saw 63". What might have happened? Try printing out the first few lines of the data file to see what the issue might be.

# TODO: Print out the first 10 lines of the data set, line by line.
# HINT: You can't use the read_csv method from pandas
# HINT: to do this manually, you'll need to use pure Python
# HINT: the open(), readline(), and close() methods should be helpful
# HINT: Use a for loop
with open('population_data.csv') as f:
    for i in range(10):
        print(f.readline())

﻿"Data Source","World Development Indicators",


"Last Updated Date","2018-06-28",


"Country Name","Country Code","Indicator Name","Indicator Code","1960","1961","1962","1963","1964","1965","1966","1967","1968","1969","1970","1971","1972","1973","1974","1975","1976","1977","1978","1979","1980","1981","1982","1983","1984","1985","1986","1987","1988","1989","1990","1991","1992","1993","1994","1995","1996","1997","1998","1999","2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016","2017",

"Aruba","ABW","Population, total","SP.POP.TOTL","54211","55438","56225","56695","57032","57360","57715","58055","58386","58726","59063","59440","59840","60243","60528","60657","60586","60366","60103","59980","60096","60567","61345","62201","62836","63026","62644","61833","61079","61032","62149","64622","68235","72504","76700","80324","83200","85451","87277","89005","90853","92898","94992","97017","98737","100031","100832","101220","101353","101453","101669","102053","102577","103187","103795","104341","104822","105264",

"Afghanistan","AFG","Population, total","SP.POP.TOTL","8996351","9166764","9345868","9533954","9731361","9938414","10152331","10372630","10604346","10854428","11126123","11417825","11721940","12027822","12321541","12590286","12840299","13067538","13237734","13306695","13248370","13053954","12749645","12389269","12047115","11783050","11601041","11502761","11540888","11777609","12249114","12993657","13981231","15095099","16172719","17099541","17822884","18381605","18863999","19403676","20093756","20966463","21979923","23064851","24118979","25070798","25893450","26616792","27294031","28004331","28803167","29708599","30696958","31731688","32758020","33736494","34656032","35530081",

"Angola","AGO","Population, total","SP.POP.TOTL","5643182","5753024","5866061","5980417","6093321","6203299","6309770","6414995","6523791","6642632","6776381","6927269","7094834","7277960","7474338","7682479","7900997","8130988","8376147","8641521","8929900","9244507","9582156","9931562","10277321","10609042","10921037","11218268","11513968","11827237","12171441","12553446","12968345","13403734","13841301","14268994","14682284","15088981","15504318","15949766","16440924","16983266","17572649","18203369","18865716","19552542","20262399","20997687","21759420","22549547","23369131","24218565","25096150","25998340","26920466","27859305","28813463","29784193",

"Albania","ALB","Population, total","SP.POP.TOTL","1608800","1659800","1711319","1762621","1814135","1864791","1914573","1965598","2022272","2081695","2135479","2187853","2243126","2296752","2350124","2404831","2458526","2513546","2566266","2617832","2671997","2726056","2784278","2843960","2904429","2964762","3022635","3083605","3142336","3227943","3286542","3266790","3247039","3227287","3207536","3187784","3168033","3148281","3128530","3108778","3089027","3060173","3051010","3039616","3026939","3011487","2992547","2970017","2947314","2927519","2913021","2905195","2900401","2895092","2889104","2880703","2876101","2873457",

"Andorra","AND","Population, total","SP.POP.TOTL","13411","14375","15370","16412","17469","18549","19647","20758","21890","23058","24276","25559","26892","28232","29520","30705","31777","32771","33737","34818","36067","37500","39114","40867","42706","44600","46517","48455","50434","52448","54509","56671","58888","60971","62677","63850","64360","64327","64142","64370","65390","67341","70049","73182","76244","78867","80991","82683","83861","84462","84449","83751","82431","80788","79223","78014","77281","76965",

The first four lines in the file are not properly formatted and don't contain data. Next, read in the data using the read_csv method. But this time, use the skiprows option.

# TODO: read in population data skipping the first four rows
# Put the results in a variable called df_population

df_population = pd.read_csv('population_data.csv', header = 2)

df_population.head()

Make sure to scroll over to see what the last column looks like. That last column, called 'Unnamed: 62', doesn't look very useful and is filled with NaN values.

pd.set_option("display.max_rows", None)
# TODO: Count the number of null values in each column
df_population.isnull().sum()

Country Name        0
Country Code        0
Indicator Name      0
Indicator Code      0
1960                4
1961                4
1962                4
1963                4
1964                4
1965                4
1966                4
1967                4
1968                4
1969                4
1970                4
1971                4
1972                4
1973                4
1974                4
1975                4
1976                4
1977                4
1978                4
1979                4
1980                4
1981                4
1982                4
1983                4
1984                4
1985                4
1986                4
1987                4
1988                4
1989                4
1990                2
1991                2
1992                3
1993                3
1994                3
1995                2
1996                2
1997                2
1998                1
1999                1
2000                1
2001                1
2002                1
2003                1
2004                1
2005                1
2006                1
2007                1
2008                1
2009                1
2010                1
2011                1
2012                2
2013                2
2014                2
2015                2
2016                2
2017                2
Unnamed: 62       264
dtype: int64

It looks like every year column has at least one NaN value.

# TODO: Count the number of null values in each row
# HINT: In the sum method, use axis=1
df_population.isnull().sum(axis = 1)

0       1
1       1
2       1
3       1
4       1
5       1
6       1
7       1
8       1
9       1
10      1
11      1
12      1
13      1
14      1
15      1
16      1
17      1
18      1
19      1
20      1
21      1
22      1
23      1
24      1
25      1
26      1
27      1
28      1
29      1
30      1
31      1
32      1
33      1
34      1
35      1
36      1
37      1
38      1
39      1
40      1
41      1
42      1
43      1
44      1
45      1
46      1
47      1
48      1
49      1
50      1
51      1
52      1
53      1
54      1
55      1
56      1
57      1
58      1
59      1
60      1
61      1
62      1
63      1
64      1
65      1
66      1
67      7
68      1
69      1
70      1
71      1
72      1
73      1
74      1
75      1
76      1
77      1
78      1
79      1
80      1
81      1
82      1
83      1
84      1
85      1
86      1
87      1
88      1
89      1
90      1
91      1
92      1
93      1
94      1
95      1
96      1
97      1
98      1
99      1
100     1
101     1
102     1
103     1
104     1
105     1
106     1
107     1
108    59
109     1
110     1
111     1
112     1
113     1
114     1
115     1
116     1
117     1
118     1
119     1
120     1
121     1
122     1
123     1
124     1
125     4
126     1
127     1
128     1
129     1
130     1
131     1
132     1
133     1
134     1
135     1
136     1
137     1
138     1
139     1
140     1
141     1
142     1
143     1
144     1
145     1
146     1
147     1
148     1
149     1
150     1
151     1
152     1
153     1
154     1
155     1
156     1
157     1
158     1
159     1
160     1
161     1
162     1
163     1
164     1
165     1
166     1
167     1
168     1
169     1
170     1
171     1
172     1
173     1
174     1
175     1
176     1
177     1
178     1
179     1
180     1
181     1
182     1
183     1
184     1
185     1
186     1
187     1
188     1
189     1
190     1
191     1
192     1
193     1
194    31
195     1
196     1
197     1
198     1
199     1
200     1
201     1
202     1
203     1
204     1
205     1
206     1
207     1
208     1
209     1
210     1
211     1
212    31
213     1
214     1
215     1
216     1
217     1
218     1
219     1
220     1
221     1
222     1
223    39
224     1
225     1
226     1
227     1
228     1
229     1
230     1
231     1
232     1
233     1
234     1
235     1
236     1
237     1
238     1
239     1
240     1
241     1
242     1
243     1
244     1
245     1
246     1
247     1
248     1
249     1
250     1
251     1
252     1
253     1
254     1
255     1
256     1
257     1
258     1
259     1
260     1
261     1
262     1
263     1
dtype: int64

And it looks like almost every row has only one null value. That is probably from the 'Unnamed: 62' column that doesn't have any relevant information in it. Next, drop the 'Unnamed: 62' column from the data frame.

# TODO: drop the 'Unnamed: 62' column from the data frame, 
# and save the results in the df_population variable

df_population.drop(columns = {'Unnamed: 62'}, inplace = True)

df_population.head()

# Run this code cell.  
# This code outputs any row that contains a null value
# The purpose is to see what rows contain null values now that 
#   'Unnamed: 62' was dropped from the data.
df_population[df_population.isnull().any(axis=1)]

Conclusion¶

This population data doesn't look too bad. Only six rows have missing values. In the transformation part of the lesson, you'll have to deal with these missing values somehow.

If you would like to see the solution file for this exercise, go to File->Open and click on 1_csv_exercise_solution.ipynb.

In the next exercise, you'll practice extracting data json and xml files.

Extract from JSON and XML¶

You'll now get practice extracting data from JSON and XML. You'll extract the same population data from the previous exercise, except the data will be in a different format.

Both JSON and XML are common formats for storing data. XML was established before JSON, and JSON has become more popular over time. They both tend to be used for sending data via web APIs, which you'll learn about later in the lesson.

Sometimes, you can obtain the same data in either JSON or XML format. The World Bank indicator data is available in either form. In this exercise, you'll use the same data except one file is formatted as JSON and the other as XML.

There is a solution file for these exercises. Go to File->Open and click on 2_extract_exercise_solution.ipynb.

Extract JSON and JSON Exercise¶

First, you'll practice extracting data from a JSON file. Run the cell below to print out the first line of the JSON file.

### 
#   Run the following cell.
#   This cell loads a function that prints the first n lines of
#   a file.
#
#   Then this function is called on the JSON file to print out
#   the first line of the population_data.json file
###

def print_lines(n, file_name):
    f = open(file_name)
    for i in range(n):
        print(f.readline())
    f.close()
    
print_lines(1, 'population_data.json')

[{"Country Name":"Aruba","Country Code":"ABW","Indicator Name":"Population, total","Indicator 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Name":"Population, total","Indicator 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Name":"Vanuatu","Country Code":"VUT","Indicator Name":"Population, total","Indicator 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Code":"XKX","Indicator Name":"Population, total","Indicator 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Name":"Yemen, Rep.","Country Code":"YEM","Indicator Name":"Population, total","Indicator 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Name":"Zambia","Country Code":"ZMB","Indicator Name":"Population, total","Indicator 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import json
with open('population_data.json','r') as jsfile:
    lines = jsfile.readlines()
    js_lines = json.loads(lines[0])

print(json.dumps(js_line[:2],indent=4))

[
    {
        "Country Name": "Aruba",
        "Country Code": "ABW",
        "Indicator Name": "Population, total",
        "Indicator Code": "SP.POP.TOTL",
        "1960": 54211.0,
        "1961": 55438.0,
        "1962": 56225.0,
        "1963": 56695.0,
        "1964": 57032.0,
        "1965": 57360.0,
        "1966": 57715.0,
        "1967": 58055.0,
        "1968": 58386.0,
        "1969": 58726.0,
        "1970": 59063.0,
        "1971": 59440.0,
        "1972": 59840.0,
        "1973": 60243.0,
        "1974": 60528.0,
        "1975": 60657.0,
        "1976": 60586.0,
        "1977": 60366.0,
        "1978": 60103.0,
        "1979": 59980.0,
        "1980": 60096.0,
        "1981": 60567.0,
        "1982": 61345.0,
        "1983": 62201.0,
        "1984": 62836.0,
        "1985": 63026.0,
        "1986": 62644.0,
        "1987": 61833.0,
        "1988": 61079.0,
        "1989": 61032.0,
        "1990": 62149.0,
        "1991": 64622.0,
        "1992": 68235.0,
        "1993": 72504.0,
        "1994": 76700.0,
        "1995": 80324.0,
        "1996": 83200.0,
        "1997": 85451.0,
        "1998": 87277.0,
        "1999": 89005.0,
        "2000": 90853.0,
        "2001": 92898.0,
        "2002": 94992.0,
        "2003": 97017.0,
        "2004": 98737.0,
        "2005": 100031.0,
        "2006": 100832.0,
        "2007": 101220.0,
        "2008": 101353.0,
        "2009": 101453.0,
        "2010": 101669.0,
        "2011": 102053.0,
        "2012": 102577.0,
        "2013": 103187.0,
        "2014": 103795.0,
        "2015": 104341.0,
        "2016": 104822.0,
        "2017": 105264.0
    },
    {
        "Country Name": "Afghanistan",
        "Country Code": "AFG",
        "Indicator Name": "Population, total",
        "Indicator Code": "SP.POP.TOTL",
        "1960": 8996351.0,
        "1961": 9166764.0,
        "1962": 9345868.0,
        "1963": 9533954.0,
        "1964": 9731361.0,
        "1965": 9938414.0,
        "1966": 10152331.0,
        "1967": 10372630.0,
        "1968": 10604346.0,
        "1969": 10854428.0,
        "1970": 11126123.0,
        "1971": 11417825.0,
        "1972": 11721940.0,
        "1973": 12027822.0,
        "1974": 12321541.0,
        "1975": 12590286.0,
        "1976": 12840299.0,
        "1977": 13067538.0,
        "1978": 13237734.0,
        "1979": 13306695.0,
        "1980": 13248370.0,
        "1981": 13053954.0,
        "1982": 12749645.0,
        "1983": 12389269.0,
        "1984": 12047115.0,
        "1985": 11783050.0,
        "1986": 11601041.0,
        "1987": 11502761.0,
        "1988": 11540888.0,
        "1989": 11777609.0,
        "1990": 12249114.0,
        "1991": 12993657.0,
        "1992": 13981231.0,
        "1993": 15095099.0,
        "1994": 16172719.0,
        "1995": 17099541.0,
        "1996": 17822884.0,
        "1997": 18381605.0,
        "1998": 18863999.0,
        "1999": 19403676.0,
        "2000": 20093756.0,
        "2001": 20966463.0,
        "2002": 21979923.0,
        "2003": 23064851.0,
        "2004": 24118979.0,
        "2005": 25070798.0,
        "2006": 25893450.0,
        "2007": 26616792.0,
        "2008": 27294031.0,
        "2009": 28004331.0,
        "2010": 28803167.0,
        "2011": 29708599.0,
        "2012": 30696958.0,
        "2013": 31731688.0,
        "2014": 32758020.0,
        "2015": 33736494.0,
        "2016": 34656032.0,
        "2017": 35530081.0
    }
]

The first "line" in the file is actually the entire file. JSON is a compact way of representing data in a dictionary-like format. Luckily, pandas has a method to read in a json file and parse the results for you.

If you open the link with the documentation, you'll see there is an orient option that can handle JSON formatted in different ways:

'split' : dict like {index -> [index], columns -> [columns], data -> [values]}

'records' : list like [{column -> value}, ... , {column -> value}]

'index' : dict like {index -> {column -> value}}

'columns' : dict like {column -> {index -> value}}

'values' : just the values array

In this case, the JSON is formatted with a 'records' orientation, so you'll need to use that value in the read_json() method. You can tell that the format is 'records' by comparing the pattern in the documentation with the pattern in the JSON file.

Next, read in the population_data.json file using pandas.

# TODO: Read in the population_data.json file using pandas's 
# read_json method. Don't forget to specific the orient option
# store the results in df_json

import pandas as pd
df_json = pd.read_json('population_data.json',orient = 'records')

# TODO: Use the head method to see the first few rows of the resulting
df_json.head()

Notice that this population data is the same as the data from the previous exercise. The column order might have changed, but the data is otherwise the same.

Extract XML¶

Next, you'll work with the same data except now the data is in xml format. Run the next code cell to see what the first fifteen lines of the data file look like.

# Run the code cell to print out the first 15 lines of the xml file
print_lines(15, 'population_data.xml')

﻿<?xml version="1.0" encoding="utf-8"?>

<Root xmlns:wb="http://www.worldbank.org">

  <data>

    <record>

      <field name="Country or Area" key="ABW">Aruba</field>

      <field name="Item" key="SP.POP.TOTL">Population, total</field>

      <field name="Year">1960</field>

      <field name="Value">54211</field>

    </record>

    <record>

      <field name="Country or Area" key="ABW">Aruba</field>

      <field name="Item" key="SP.POP.TOTL">Population, total</field>

      <field name="Year">1961</field>

      <field name="Value">55438</field>

    </record>

XML looks very similar to HTML. XML is formatted with tags having values inside the tags. XML is not as easy to navigate as JSON. Pandas cannot read in XML directly. One reason is that tag names are user defined. Every XML file might have different formatting. You can imagine why XML has fallen out of favor relative to JSON.

How to read and navigate XML¶

There is a Python library called BeautifulSoup, which makes reading in and parsing XML data easier. Here is the link to the documentation: Beautiful Soup Documentation

The find() method will find the first place where an xml element occurs. For example using find('record') will return the first record in the xml file:

<record>
  <field name="Country or Area" key="ABW">Aruba</field>
  <field name="Item" key="SP.POP.TOTL">Population, total</field>
  <field name="Year">1960</field>
  <field name="Value">54211</field>
</record>

The find_all() method returns all of the matching tags. So find_all('record') would return all of the elements with the <record> tag.

Run the code cells below to get a basic idea of how to navigate XML with BeautifulSoup. To navigate through the xml file, you search for a specific tag using the find() method or find_all() method.

Below these code cells, there is an exercise for wrangling the XML data.

# import the BeautifulSoup library
from bs4 import BeautifulSoup

# open the population_data.xml file and load into Beautiful Soup
with open("population_data.xml") as fp:
    soup = BeautifulSoup(fp, "lxml") # lxml is the Parser type

# output the first 5 records in the xml file
# this is an example of how to navigate the XML document with BeautifulSoup

i = 0
# use the find_all method to get all record tags in the document
for record in soup.find_all('record'):
    # use the find_all method to get all fields in each record
    i += 1
    for field in record.find_all('field'):
        print(field['name'], ': ' , field.text)
    print()
    if i == 5:
        break

Country or Area :  Aruba
Item :  Population, total
Year :  1960
Value :  54211

Country or Area :  Aruba
Item :  Population, total
Year :  1961
Value :  55438

Country or Area :  Aruba
Item :  Population, total
Year :  1962
Value :  56225

Country or Area :  Aruba
Item :  Population, total
Year :  1963
Value :  56695

Country or Area :  Aruba
Item :  Population, total
Year :  1964
Value :  57032

XML Exercise (Challenge)¶

Create a data frame from the xml file. This exercise is somewhat tricky. One solution would be to convert the xml data into dictionaries and then use the dictionaries to create a data frame.

The dataframe should have the following layout:

Country or Area	Year	Item	Value
Aruba	1960	Population, total	54211
Aruba	1961	Population, total	55348

etc...

Technically, extracting XML, transforming the results, and putting it into a data frame is a full ETL pipeline. This exercise is jumping ahead in terms of what's to come later in the lesson. But it's a good chance to familiarize yourself with XML.

# TODO: Create a pandas data frame from the XML data.
# HINT: You can use dictionaries to create pandas data frames.
# HINT: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.from_dict.html#pandas.DataFrame.from_dict
# HINT: You can make a dictionary for each column or for each row (See the link above for more information)
# HINT: Modify the code from the previous code cell
with open("population_data.xml") as fp:
    soup = BeautifulSoup(fp, "lxml") # lxml is the Parser type

full_dict = {}
i = 0
for record in soup.find_all('record'):
    record_list = []
    for field in record.find_all('field'):
        record_list.append(field.text)
    full_dict[str(i)] = record_list
    i = i+1

df = pd.DataFrame.from_dict(full_dict, orient='index', columns = ['Country or Area','Item','Year','Value'])     

df = df.pivot(index = 'Country or Area', columns = 'Year',values = 'Value')
df.reset_index(level = 0, inplace = True)

Conclusion¶

Like CSV, JSON and XML are ways to format data. If everything is formatted correctly, JSON is especially easy to work with. XML is an older standard and a bit trickier to handle.

As a reminder, there is a solution file for these exercises. You can go to File->Open and then click on 2_extract_exercise.

Extract Data from SQL Databases¶

In this workbook, you'll gain experience extracting data from SQL databases. This is an overview of Python tools and assumes you already have experience writing SQL queries.

Pandas and sqlite3¶

You can use Pandas to open a SQL database or to run a SQL query against a database. There is more than one way to do this depending on the type of SQL database you are working with: the sqlite3 library or the sqlalchemy library.

In the same folder as this Jupyter notebook, there is a SQLite database file called "population_data.db". SQLite is a database engine meant for single applications. The entire database is contained in one file. You can read more about SQLite here.

In this example, the "population_data.db" database contains only one table called "population_data". Run the code in the following cells to see how to use a SQLite database with pandas. (If you're curious how the data was converted from a csv file to a database, go to File->Open and click on create_db.py). You'll find an exercise at the bottom of the Jupyter notebook.

Demo: SQLite3 and Pandas¶

import sqlite3
import pandas as pd

# connect to the database
conn = sqlite3.connect('population_data.db')

# run a query
pd.read_sql('SELECT * FROM population_data', conn)

pd.read_sql('SELECT "Country_Name", "Country_Code", "1960" FROM population_data', conn)

Demo: SQLAlchemy and Pandas¶

If you are working with a different type of database such as MySQL or PostgreSQL, you can use the SQLAlchemy library with pandas. Here are the instructions for connecting to different types of databases using SQLAlchemy.

Run the code below to see how to connect to the population_data.db database.

import os
print(os.getcwd())

/Users/xuhao3/UDACITY/Data_Engineering

import pandas as pd
from sqlalchemy import create_engine

### 
# create a database engine 
# to find the correct file path, use the python os library:
# import os
# print(os.getcwd())
#
###

engine = create_engine('sqlite:///population_data.db')
pd.read_sql("SELECT * FROM population_data", engine)

Exercise¶

Connect to the population_data.db SQLite database, and answer the following questions:

Write a query that finds the change in population from 1960 to 1961 in Aruba
Write a query that finds the population of Belgium and also Luxembourg in 1975. The output should have two rows.

There is a solution if you go to File->Open->3_sql_exercise_solution.ipynb

pd.read_sql('SELECT "1960","1961" FROM population_data where "Country_Name" = "Aruba"', engine)

pd.read_sql('SELECT "Country_Name","1975" FROM population_data where "Country_Name" = "Belgium"OR "Country_Name" ="Luxembourg"', engine)

APIs¶

Instead of downloading World Bank data via a csv file, you're going to download the data using the World Bank APIs. The purpose of this exercise is to gain experience with another way of extracting data.

API is an acronym that stands for application programming interface. API’s provide a standardized way for two applications to talk to each other. In this case, the applications communicating with each other are the server application where World Bank stores data and your Jupyter notebook.

If you wanted to pull data directly from the World Bank’s server, you’d have to know what database system the World Bank was using. You’d also need permission to log in directly to the server, which would be a security risk for the World Bank. And if the World Bank ever migrated its data to a new system, you would have to rewrite all of your code again. The API allows you to execute code on the World Bank server without getting direct access.

Before there were APIs¶

Before there were APIs, there was web scraping. People would download html directly from a website and then parse the results programatically. This practice is in a legal grey area. One reason that APIs became popular was so that companies could provide data to users and discourage web scraping.

Here are a few articles about the legality of web scraping.

All sorts of companies have public facing APIs including Facebook, Twitter, Google and Pinterest. You can pull data from these companies to create your own applications.

In this notebook, you’ll get practice using Python to pull data from the World Bank indicators API.

Here are links to information about the World Bank indicators and projects APIs if you want to learn more:

Using APIs¶

In general, you access APIs via the web using a web address. Within the web address, you specify the data that you want. To know how to format the web address, you need to read an API's documentation. Some APIs also require that you send login credentials as part of your request. The World Bank APIs are public and do not require login credentials.

The Python requests library makes working with APIs relatively simple.

Example Indicators API¶

Run the code example below to request data from the World Bank Indicators API. According to the documntation, you format your request url like so:

http://api.worldbank.org/v2/countries/ + list of country abbreviations separated by ; + /indicators/ + indicator name + ? + options

where options can include

per_page - number of records to return per page
page - which page to return - eg if there are 5000 records and 100 records per page
date - filter by dates
format - json or xml

and a few other options that you can read about here.

import requests
import pandas as pd

url = 'http://api.worldbank.org/v2/countries/br;cn;us;de/indicators/SP.POP.TOTL/?format=json&per_page=1000'
r = requests.get(url)
r.json()

[{'page': 1,
  'pages': 1,
  'per_page': 1000,
  'total': 240,
  'sourceid': '2',
  'lastupdated': '2019-12-20'},
 [{'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2019',
   'value': None,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2018',
   'value': 209469333,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2017',
   'value': 207833831,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2016',
   'value': 206163058,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2015',
   'value': 204471769,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2014',
   'value': 202763735,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2013',
   'value': 201035903,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2012',
   'value': 199287296,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2011',
   'value': 197514534,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2010',
   'value': 195713635,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2009',
   'value': 193886508,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2008',
   'value': 192030362,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2007',
   'value': 190130443,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2006',
   'value': 188167356,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2005',
   'value': 186127103,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2004',
   'value': 184006481,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2003',
   'value': 181809246,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2002',
   'value': 179537520,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2001',
   'value': 177196054,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '2000',
   'value': 174790340,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1999',
   'value': 172318675,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1998',
   'value': 169785250,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1997',
   'value': 167209040,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1996',
   'value': 164614688,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1995',
   'value': 162019896,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1994',
   'value': 159432716,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1993',
   'value': 156849078,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1992',
   'value': 154259380,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1991',
   'value': 151648011,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1990',
   'value': 149003223,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1989',
   'value': 146328304,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1988',
   'value': 143627503,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1987',
   'value': 140891602,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1986',
   'value': 138108912,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1985',
   'value': 135274080,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1984',
   'value': 132383568,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1983',
   'value': 129448819,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1982',
   'value': 126498314,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1981',
   'value': 123570327,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1980',
   'value': 120694009,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1979',
   'value': 117878411,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1978',
   'value': 115121153,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1977',
   'value': 112425392,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1976',
   'value': 109790938,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1975',
   'value': 107216205,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1974',
   'value': 104706198,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'BR', 'value': 'Brazil'},
   'countryiso3code': 'BRA',
   'date': '1973',
   'value': 102259497,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
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   'country': {'id': 'BR', 'value': 'Brazil'},
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   'date': '1995',
   'value': 266278000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1994',
   'value': 263126000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1993',
   'value': 259919000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1992',
   'value': 256514000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1991',
   'value': 252981000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1990',
   'value': 249623000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1989',
   'value': 246819000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1988',
   'value': 244499000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1987',
   'value': 242289000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1986',
   'value': 240133000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1985',
   'value': 237924000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1984',
   'value': 235825000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1983',
   'value': 233792000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1982',
   'value': 231664000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1981',
   'value': 229466000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1980',
   'value': 227225000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1979',
   'value': 225055000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1978',
   'value': 222585000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1977',
   'value': 220239000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1976',
   'value': 218035000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1975',
   'value': 215973000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1974',
   'value': 213854000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1973',
   'value': 211909000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1972',
   'value': 209896000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1971',
   'value': 207661000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1970',
   'value': 205052000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1969',
   'value': 202677000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1968',
   'value': 200706000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1967',
   'value': 198712000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1966',
   'value': 196560000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1965',
   'value': 194303000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1964',
   'value': 191889000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1963',
   'value': 189242000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1962',
   'value': 186538000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1961',
   'value': 183691000,
   'unit': '',
   'obs_status': '',
   'decimal': 0},
  {'indicator': {'id': 'SP.POP.TOTL', 'value': 'Population, total'},
   'country': {'id': 'US', 'value': 'United States'},
   'countryiso3code': 'USA',
   'date': '1960',
   'value': 180671000,
   'unit': '',
   'obs_status': '',
   'decimal': 0}]]

###
# Run this cell that converts the json into a dataframe
# Note that you do not need the pd.read_json() method because this is not a file or a string containing json 
##

pd.DataFrame(r.json()[1])

This json data isn't quite ready for a pandas data frame. Notice that the json response is a list with two entries. The first entry is

{''page': 1,
  'pages': 1,
  'per_page': 1000,
  'total': 240,
  'sourceid': '2',
  'lastupdated': '2019-12-20'}

That first entry is meta data about the results. For example, it says that there is one page returned with 232 results.

The second entry is another list containing the data. This data would need some cleaning to be used in a pandas data frame. That would happen later in the transformation step of an ETL pipeline. Run the cell below to read the results into a dataframe and see what happens.

###
# Run this cell that converts the json into a dataframe
# Note that you do not need the pd.read_json() method because this is not a file or a string containing json 
##

pd.DataFrame(r.json()[1])

There are some issues with this dataframe. The country and indicator variables don't look particularly useful in their current form. Again, dealing with those issues would come in the transformation phase of a pipeline, which comes later in the lesson.

Exercise Indicators API¶

Use the Indicators API to request rural population data for Switzerland in the years 1995 through 2001. Here are a few helpful resources:

To find the indicator code, first search for the indicator here: https://data.worldbank.org Click on the indicator name. The indicator code is in the url. For example, the indicator code for total population is SP.POP.TOTL, which you can see in the link https://data.worldbank.org/indicator/SP.RUR.TOTL.

# TODO: get the url ready
url = 'http://api.worldbank.org/v2/countries/ch/indicators/SP.RUR.TOTL/?format=json&per_page=1000&date=1995:2001'

# TODO: send the request
r = requests.get(url)

# TODO: output the json using the json method like in the previous example
pd.DataFrame(r.json()[1])

Combining Data¶

Practice combining data from two different data sets. In the same folder as this Jupyter notebook, there are two csv files:

rural_population_percent.csv
electricity_access_percent.csv

They both come from the World Bank Indicators data.

The rural populaton data represents the percent of a country's population that is rural over time. The electricity access data shows the percentage of people with access to electricity.

In this exercise, you will combine these two data sets together into one pandas data frame.

Exercise 1¶

Combine the two data sets using the pandas concat method. In other words, find the union of the two data sets.

# TODO: import the pandas library
import pandas as pd
# TODO: read in each csv file into a separate variable
# HINT: remember from the Extract material that these csv file have some formatting issues
# HINT: The file paths are 'rural_population_percent.csv' and 'electricity_access_percent.csv'
df_rural = pd.read_csv('rural_population_percent.csv', header = 2)
df_electricity = pd.read_csv('electricity_access_percent.csv', header = 2)

# TODO: remove the 'Unnamed:62' column from each data set
df_rural.drop(columns = {'Unnamed: 62'},inplace = True)
df_electricity.drop(columns = {'Unnamed: 62'},inplace = True)

# TODO: combine the two data sets together using the concat method
df_all = pd.concat([df_rural, df_electricity], axis = 0, ignore_index = True)

# In other words, all of the rows of df_rural will come first
# followed by all the rows in df_electricity. This is possible to do
# because they both have the same column names.

Exercise 2 (Challenge)¶

This exercise is more challenging.

Combine the two datas in the csv file together so that the output looks like the following:

Country Name	Country Code	Year	Rural_Value	Electricity_Value
Aruba	ABW	1960	49.224	49.239

... etc.

Order the results in the dataframe by country and then by year

Here are a few pandas methods that should be helpful:

HINT: You can use country name, country code, and the year as common keys between the data sets

df_all.head(1)

df_rural.columns

Index(['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code',
       '1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967', '1968',
       '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976', '1977',
       '1978', '1979', '1980', '1981', '1982', '1983', '1984', '1985', '1986',
       '1987', '1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',
       '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003', '2004',
       '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013',
       '2014', '2015', '2016', '2017'],
      dtype='object')

# TODO: merge the data sets together according to the instructions. First, use the 
# melt method to change the formatting of each data frame so that it looks like this:
# Country Name, Country Code, Year, Rural Value
# Country Name, Country Code, Year, Electricity Value
df_rural = pd.melt(df_rural, id_vars = df_rural.columns[:4],
                    var_name ='Year', value_name = 'Rural_Value')
df_electricity = pd.melt(df_electricity, id_vars = df_electricity.columns[:4],
                          var_name ='Year', value_name = 'Electricity_Value')




# TODO: drop any columns from the data frames that aren't needed
df_rural.drop(columns = {'Indicator Name', 'Indicator Code'}, inplace = True)
df_electricity.drop(columns = {'Indicator Name', 'Indicator Code'}, inplace = True)

df_electricity.head()

# TODO: merge the data frames together based on their common columns
# in this case, the common columns are Country Name, Country Code, and Year

df_combined = df_rural.merge(df_electricity, on = ['Country Name', 'Country Code', 'Year'],
                            how = 'outer')

# TODO: sort the results by country and then by year
df_combined = df_combined.sort_values(['Country Name', 'Year'])

df_combined

Cleaning Data¶

Cleaning data is a big topic. Every data set might have its own issues whether that involves missing values, duplicated entries, data entry mistakes, etc. In this exercise, you'll do some data cleaning on the World Bank projects and World Bank indicators data sets.

Currently, the projects data and the indicators data have different values for country names. Your task in this exercise is to clean both data sets so that they have consistent country names. This will allow you to join the two data sets together. Cleaning data, unfortunately, can be tedious and take a lot of your time as a data scientist.

Why might you want to join these data sets together? What if, for example, you wanted to run linear regression to try to predict project costs based on indicator data? Or you might want to analyze the types of projects that get approved versus the indicator data. For example, do countries with low rates of rural electrification have more rural themed projects?

Part 1 - Explore the Data¶

Run the code cells below to import the data sets.

import pandas as pd

# read in the population data and drop the final column
df_indicator = pd.read_csv('population_data.csv', skiprows=4)
df_indicator.drop(['Unnamed: 62'], axis=1, inplace=True)

# read in the projects data set with all columns type string
df_projects = pd.read_csv('projects_data.csv', dtype=str)
df_projects.drop(['Unnamed: 56'], axis=1, inplace=True)

The next code cell outputs the unique country names and ISO abbreviations in the population indicator data set. You'll notice a few values that represent world regions such as 'East Asia & Pacific' and 'East Asia & Pacific (excluding high income)'.

df_indicator[['Country Name', 'Country Code']].drop_duplicates()

Run the next code cell to see the unique country names in the project data set. Notice that the projects data has two columns for country name. One is called 'countryname' and the other is called 'Country'. The 'Country' column only has NaN values.

Another thing of note: It would've been easier to join the two data sets together if the projects data had the ISO country abbreviations like the indicator data has. Unfortunately, the projects data does not have the ISO country abbreviations. To join these two data sets together, you essentially have two choices:

add a column of ISO 3 codes to the projects data set find the difference between the projects data country names and indicator data country names. Then clean the data so that they are the same. Run the code cell below to see what the project countries look like:

df_projects['countryname'].unique()

array(['World;World',
       'Democratic Republic of the Congo;Democratic Republic of the Congo',
       "People's Republic of Bangladesh;People's Republic of Bangladesh",
       'Islamic  Republic of Afghanistan;Islamic  Republic of Afghanistan',
       'Federal Republic of Nigeria;Federal Republic of Nigeria',
       'Republic of Tunisia;Republic of Tunisia',
       'Lebanese Republic;Lebanese Republic',
       'Democratic Socialist Republic of Sri Lan;Democratic Socialist Republic of Sri Lan',
       'Nepal;Nepal', 'Kyrgyz Republic;Kyrgyz Republic',
       'Hashemite Kingdom of Jordan;Hashemite Kingdom of Jordan',
       'Republic of the Union of Myanmar;Republic of the Union of Myanmar',
       'Arab Republic of Egypt;Arab Republic of Egypt',
       'United Republic of Tanzania;United Republic of Tanzania',
       'Federal Democratic Republic of Ethiopia;Federal Democratic Republic of Ethiopia',
       'Burkina Faso;Burkina Faso',
       'Republic of Uzbekistan;Republic of Uzbekistan', 'Romania;Romania',
       'Republic of Ghana;Republic of Ghana',
       'Republic of Ecuador;Republic of Ecuador',
       'Socialist Republic of Vietnam;Socialist Republic of Vietnam',
       "People's Republic of China;People's Republic of China",
       'Republic of Senegal;Republic of Senegal', 'Grenada;Grenada',
       'Co-operative Republic of Guyana;Co-operative Republic of Guyana',
       'Central Africa;Central Africa',
       'Republic of Indonesia;Republic of Indonesia',
       'Republic of Kenya;Republic of Kenya',
       'Kingdom of Cambodia;Kingdom of Cambodia',
       'Republic of Angola;Republic of Angola',
       'Republic of India;Republic of India', 'Africa;Africa',
       'Republic of Zambia;Republic of Zambia',
       'Republic of Panama;Republic of Panama',
       'Kingdom of Tonga;Kingdom of Tonga',
       'Islamic Republic of Pakistan;Islamic Republic of Pakistan',
       'Republic of Niger;Republic of Niger',
       'Republic of Uganda;Republic of Uganda',
       'Republic of Madagascar;Republic of Madagascar',
       'Plurinational State of Bolivia;Plurinational State of Bolivia',
       'Kingdom of Morocco;Kingdom of Morocco',
       'Republic of Djibouti;Republic of Djibouti',
       'Somali Democratic Republic;Somali Democratic Republic',
       'Republic of Maldives;Republic of Maldives',
       'Central African Republic;Central African Republic',
       'Western Africa;Western Africa',
       'Republic of Cabo Verde;Republic of Cabo Verde',
       "Republic of Cote d'Ivoire;Republic of Cote d'Ivoire",
       'Montenegro;Montenegro', 'West Bank and Gaza;West Bank and Gaza',
       'Republic of Haiti;Republic of Haiti',
       'Republic of Guinea-Bissau;Republic of Guinea-Bissau',
       'Samoa;Samoa', 'Republic of Congo;Republic of Congo',
       'Federated States of Micronesia;Federated States of Micronesia',
       'Republic of the Marshall Islands;Republic of the Marshall Islands',
       'Republic of Rwanda;Republic of Rwanda',
       'Republic of Albania;Republic of Albania',
       'Republic of Burundi;Republic of Burundi',
       'Argentine Republic;Argentine Republic',
       'Pacific Islands;Pacific Islands',
       'Republic of Turkey;Republic of Turkey',
       'Democratic Republic of Sao Tome and Prin;Democratic Republic of Sao Tome and Prin',
       'Republic of The Gambia;Republic of The Gambia',
       "Lao People's Democratic Republic;Lao People's Democratic Republic",
       'Republic of Liberia;Republic of Liberia',
       'Republic of Benin;Republic of Benin',
       'Republic of Mozambique;Republic of Mozambique',
       'Kingdom of Bhutan;Kingdom of Bhutan',
       'Republic of Cameroon;Republic of Cameroon',
       'Republic of Chad;Republic of Chad',
       'Republic of Guinea;Republic of Guinea',
       'Republic of Yemen;Republic of Yemen',
       'Commonwealth of Dominica;Commonwealth of Dominica',
       'Republic of Palau;Republic of Palau',
       'Republic of Malawi;Republic of Malawi',
       'Republic of Serbia;Republic of Serbia',
       'Republic of Iraq;Republic of Iraq',
       'Republic of the Philippines;Republic of the Philippines',
       'Solomon Islands;Solomon Islands',
       'Republic of Fiji;Republic of Fiji',
       'United Mexican States;United Mexican States',
       'Republic of Colombia;Republic of Colombia',
       'Republic of Peru;Republic of Peru',
       'Republic of Mali;Republic of Mali',
       'Islamic Republic of Mauritania;Islamic Republic of Mauritania',
       'Republic of Nicaragua;Republic of Nicaragua',
       'Republic of Belarus;Republic of Belarus', 'Jamaica;Jamaica',
       'Ukraine;Ukraine', 'Republic of Togo;Republic of Togo',
       'Republic of Guatemala;Republic of Guatemala',
       'Republic of Sierra Leone;Republic of Sierra Leone',
       'Republic of Moldova;Republic of Moldova',
       'Republic of Armenia;Republic of Armenia',
       'Republic of Costa Rica;Republic of Costa Rica',
       'Republic of Tajikistan;Republic of Tajikistan',
       'Latin America;Latin America',
       'Federative Republic of Brazil;Federative Republic of Brazil',
       'Bosnia and Herzegovina;Bosnia and Herzegovina',
       'Republic of Suriname;Republic of Suriname', 'Mongolia;Mongolia',
       'Oriental Republic of Uruguay;Oriental Republic of Uruguay',
       'Republic of Kiribati;Republic of Kiribati',
       'Kingdom of Lesotho;Kingdom of Lesotho', 'Tuvalu;Tuvalu',
       'Southern Africa;Southern Africa', 'Georgia;Georgia',
       'Gabonese Republic;Gabonese Republic',
       'Republic of Chile;Republic of Chile',
       'Republic of Kosovo;Republic of Kosovo',
       'Dominican Republic;Dominican Republic',
       'Organization of Eastern Caribbean States;Organization of Eastern Caribbean States',
       'Caribbean;Caribbean', 'Republic of Zimbabwe;Republic of Zimbabwe',
       'Republic of Mauritius;Republic of Mauritius',
       'The Independent State of Papua New Guine;The Independent State of Papua New Guine',
       'Republic of Vanuatu;Republic of Vanuatu',
       'Eastern Africa;Eastern Africa',
       'St. Vincent and the Grenadines;St. Vincent and the Grenadines',
       'Republic of Honduras;Republic of Honduras',
       'Republic of South Sudan;Republic of South Sudan',
       'Republic of Croatia;Republic of Croatia',
       'Democratic Republic of Timor-Leste;Democratic Republic of Timor-Leste',
       'Republic of South Africa;Republic of South Africa',
       'Republic of Botswana;Republic of Botswana',
       'Republic of Kazakhstan;Republic of Kazakhstan',
       'Republic of Azerbaijan;Republic of Azerbaijan',
       'Kingdom of Swaziland;Kingdom of Swaziland',
       'Union of the Comoros;Union of the Comoros',
       'Turkmenistan;Turkmenistan',
       'Europe and Central Asia;Europe and Central Asia',
       'South Asia;South Asia',
       'Macedonia; former Yugoslav Republic of;Macedonia; former Yugoslav Republic of',
       'St. Lucia;St. Lucia', 'Belize;Belize',
       'Republic of Namibia;Republic of Namibia',
       'Republic of the Sudan;Republic of the Sudan',
       'Republic of Paraguay;Republic of Paraguay',
       'Western Balkans;Western Balkans',
       'East Asia and Pacific;East Asia and Pacific',
       'Republic of Bulgaria;Republic of Bulgaria',
       'Central Asia;Central Asia',
       'Republic of Seychelles;Republic of Seychelles',
       'Kingdom of Thailand;Kingdom of Thailand',
       'Republic of Poland;Republic of Poland',
       'Multi-Regional;Multi-Regional',
       'Republic of El Salvador;Republic of El Salvador',
       'Caucasus;Caucasus', 'Russian Federation;Russian Federation',
       'Middle East and North Africa;Middle East and North Africa',
       'Antigua and Barbuda;Antigua and Barbuda',
       'Central America;Central America',
       'Commonwealth of The Bahamas;Commonwealth of The Bahamas',
       'Republic of Trinidad and Tobago;Republic of Trinidad and Tobago',
       "Socialist People's Libyan Arab Jamahiriy;Socialist People's Libyan Arab Jamahiriy",
       'Czech Republic;Czech Republic',
       'Republic of Latvia;Republic of Latvia', 'Mekong;Mekong',
       'American Samoa;American Samoa',
       'Syrian Arab Republic;Syrian Arab Republic', 'Asia;Asia',
       'Andean Countries;Andean Countries', 'Hungary;Hungary',
       'Republic of Equatorial Guinea;Republic of Equatorial Guinea',
       "People's Democratic Republic of Algeria;People's Democratic Republic of Algeria",
       'Barbados;Barbados', 'Malaysia;Malaysia',
       'State of Eritrea;State of Eritrea', 'Mercosur;Mercosur',
       'Slovak Republic;Slovak Republic',
       'Republica Bolivariana de Venezuela;Republica Bolivariana de Venezuela',
       'Islamic Republic of Iran;Islamic Republic of Iran',
       'Republic of Lithuania;Republic of Lithuania',
       'St. Kitts and Nevis;St. Kitts and Nevis',
       'Republic of Korea;Republic of Korea',
       'Republic of Slovenia;Republic of Slovenia',
       'Republic of Estonia;Republic of Estonia',
       'Red Sea and Gulf of Aden;Red Sea and Gulf of Aden',
       'Aral Sea;Aral Sea', 'Republic of Cyprus;Republic of Cyprus',
       'Socialist Federal Republic of Yugoslavia;Socialist Federal Republic of Yugoslavia',
       'Portuguese Republic;Portuguese Republic',
       'Sultanate of Oman;Sultanate of Oman',
       'Hellenic Republic;Hellenic Republic',
       'Kingdom of Spain;Kingdom of Spain', 'Ireland;Ireland',
       'Republic of Singapore;Republic of Singapore',
       'State of Israel;State of Israel',
       'Republic of Finland;Republic of Finland',
       'Republic of Iceland;Republic of Iceland',
       'New Zealand;New Zealand', 'Taiwan; China;Taiwan; China',
       'EU Accession Countries;EU Accession Countries',
       'Sint Maarten;Sint Maarten', 'Japan;Japan',
       'Italian Republic;Italian Republic',
       'Kingdom of Norway;Kingdom of Norway',
       'Republic of Malta;Republic of Malta',
       'Kingdom of Denmark;Kingdom of Denmark',
       'Republic of Austria;Republic of Austria',
       'Commonwealth of Australia;Commonwealth of Australia',
       'Kingdom of Belgium;Kingdom of Belgium',
       'Kingdom of the Netherlands;Kingdom of the Netherlands',
       'Grand Duchy of Luxembourg;Grand Duchy of Luxembourg',
       'French Republic;French Republic'], dtype=object)

Part 2 - Use the Pycountry library¶

Did you notice a pattern in the projects data country names? The entries are repeated and separated by a semi-colon like this:

'Kingdom of Spain;Kingdom of Spain'
'New Zealand;New Zealand'

The first step is to clean the country name column and get rid of the semi-colon. Do that below:

### 
#
# TODO: In the df_projects dataframe, create a new column called 'Official Country Name' so that the country name only appears once. 
# For example, `Republic of Malta;Republic of Malta` should be `Republic of Malta`.
#
# HINT: use the split() method - see https://pandas.pydata.org/pandas-docs/stable/text.html for examples
# HINT: with pandas, you can do all of this with just one line of code
###

df_projects['Official Country Name'] = df_projects['countryname'].apply(lambda x:x.split(';')[0])

It looks like the projects data set has official country names. Hence, this data set has an entry like "Kingdom of Spain" whereas the indicators data has just "Spain".

Luckily, someone has developed a Python library called pycountry. This library has country names, ISO abbreviations, and official country names. While you might not be able to clean all of the data with the help of this Python library, it will probably help.

Run the code cells below to install the pycountry library and see how it works.

# Run this code cell to install and import the pycountry library
!pip install pycountry
from pycountry import countries

Collecting pycountry
  Downloading pycountry-19.8.18.tar.gz (10.0 MB)
     |████████████████████████████████| 10.0 MB 5.3 MB/s eta 0:00:01
Building wheels for collected packages: pycountry
  Building wheel for pycountry (setup.py) ... done
  Created wheel for pycountry: filename=pycountry-19.8.18-py2.py3-none-any.whl size=10627361 sha256=1dc062a6c5de54c0fa5ec1140554bc114e02415d661164804e833e755b1ae3de
  Stored in directory: /Users/xuhao3/Library/Caches/pip/wheels/00/77/65/5f318c3c30b046b450a385ecfc690125f8a270cdb0f3f483b4
Successfully built pycountry
Installing collected packages: pycountry
Successfully installed pycountry-19.8.18

# Run this code cell to see an example of how the library works
countries.get(name='Spain')

Country(alpha_2='ES', alpha_3='ESP', name='Spain', numeric='724', official_name='Kingdom of Spain')

# Run this code cell to see how you can also look up countries without specifying the key
countries.lookup('Kingdom of Spain').alpha_3

'ESP'

The goal is to add the ISO codes to the projects data set. To start, use the pycountry library to make a dictionary mapping the unique countries in 'Official Country Name' to the ISO code.

Iterate through the unique countries in df_projects['Official Country Name']. Create a dictionary mapping the 'Country Name' to the alpha_3 ISO abbreviations.

The dictionary should look like: {'Kingdom of Spain':'ESP'}

If a country name cannot be found in the pycountry library, add it to a list called country_not_found.

# set up the libraries and variables
from collections import defaultdict
country_not_found = [] # stores countries not found in the pycountry library
project_country_abbrev_dict = defaultdict(str) # set up an empty dictionary of string values

# TODO: iterate through the country names in df_projects. 
# Create a dictionary mapping the country name to the alpha_3 ISO code
for country in df_projects['Official Country Name'].drop_duplicates().sort_values():
    try: 
        # TODO: look up the country name in the pycountry library
        # store the country name as the dictionary key and the ISO-3 code as the value
        project_country_abbrev_dict[country] = countries.lookup(country).alpha_3
    except:
        # If the country name is not in the pycountry library, then print out the country name
        # And store the results in the country_not_found list
        print(country, ' not found')
        country_not_found.append(country)

Africa  not found
Andean Countries  not found
Aral Sea  not found
Asia  not found
Caribbean  not found
Caucasus  not found
Central Africa  not found
Central America  not found
Central Asia  not found
Co-operative Republic of Guyana  not found
Commonwealth of Australia  not found
Democratic Republic of Sao Tome and Prin  not found
Democratic Republic of the Congo  not found
Democratic Socialist Republic of Sri Lan  not found
EU Accession Countries  not found
East Asia and Pacific  not found
Eastern Africa  not found
Europe and Central Asia  not found
Islamic  Republic of Afghanistan  not found
Kingdom of Swaziland  not found
Latin America  not found
Macedonia  not found
Mekong  not found
Mercosur  not found
Middle East and North Africa  not found
Multi-Regional  not found
Organization of Eastern Caribbean States  not found
Oriental Republic of Uruguay  not found
Pacific Islands  not found
Red Sea and Gulf of Aden  not found
Republic of Congo  not found
Republic of Cote d'Ivoire  not found
Republic of Korea  not found
Republic of Kosovo  not found
Republic of Niger  not found
Republic of Rwanda  not found
Republic of Togo  not found
Republic of the Union of Myanmar  not found
Republica Bolivariana de Venezuela  not found
Sint Maarten  not found
Socialist Federal Republic of Yugoslavia  not found
Socialist People's Libyan Arab Jamahiriy  not found
Socialist Republic of Vietnam  not found
Somali Democratic Republic  not found
South Asia  not found
Southern Africa  not found
St. Kitts and Nevis  not found
St. Lucia  not found
St. Vincent and the Grenadines  not found
State of Eritrea  not found
The Independent State of Papua New Guine  not found
West Bank and Gaza  not found
Western Africa  not found
Western Balkans  not found
World  not found

Quite a few country names were not in the pycountry library. Some of these are regions like "South Asia" or "Southern Africa", so it makes sense that these would not show up in the pycountry library.

Part 3 - Making a Manual Mapping¶

Perhaps some of these missing df_projects countries are already in the indicators data set. In the next cell, check if any of the countries in the country_not_found list are in the indicator list of countries.

# Run this code cell to 
# iterate through the country_not_found list and check if the country name is in the df_indicator data set
indicator_countries = df_indicator[['Country Name', 'Country Code']].drop_duplicates().sort_values(by='Country Name')

for country in country_not_found:
    if country in indicator_countries['Country Name'].tolist():
        print(country)

South Asia
St. Kitts and Nevis
St. Lucia
St. Vincent and the Grenadines
West Bank and Gaza
World

Unfortunately, there aren't too many country names that match between df_indicator and df_projects. This is where data cleaning becomes especially tedious, but in this case, we've done a lot of the work for you.

We've manually created a dictionary that maps all of the countries in country_not_found to the ISO-3 alpha codes. You could try to do this programatically using some sophisticated string matching rules. That might be worth your time for a larger data set. But in this case, it's probably faster to type out the dictionary.

# run this code cell to load the dictionary

country_not_found_mapping = {'Co-operative Republic of Guyana': 'GUY',
             'Commonwealth of Australia':'AUS',
             'Democratic Republic of Sao Tome and Prin':'STP',
             'Democratic Republic of the Congo':'COD',
             'Democratic Socialist Republic of Sri Lan':'LKA',
             'East Asia and Pacific':'EAS',
             'Europe and Central Asia': 'ECS',
             'Islamic  Republic of Afghanistan':'AFG',
             'Latin America':'LCN',
              'Caribbean':'LCN',
             'Macedonia':'MKD',
             'Middle East and North Africa':'MEA',
             'Oriental Republic of Uruguay':'URY',
             'Republic of Congo':'COG',
             "Republic of Cote d'Ivoire":'CIV',
             'Republic of Korea':'KOR',
             'Republic of Niger':'NER',
             'Republic of Kosovo':'XKX',
             'Republic of Rwanda':'RWA',
              'Republic of The Gambia':'GMB',
              'Republic of Togo':'TGO',
              'Republic of the Union of Myanmar':'MMR',
              'Republica Bolivariana de Venezuela':'VEN',
              'Sint Maarten':'SXM',
              "Socialist People's Libyan Arab Jamahiriy":'LBY',
              'Socialist Republic of Vietnam':'VNM',
              'Somali Democratic Republic':'SOM',
              'South Asia':'SAS',
              'St. Kitts and Nevis':'KNA',
              'St. Lucia':'LCA',
              'St. Vincent and the Grenadines':'VCT',
              'State of Eritrea':'ERI',
              'The Independent State of Papua New Guine':'PNG',
              'West Bank and Gaza':'PSE',
              'World':'WLD'}

# Next, update the project_country_abbrev_dict variable with these new values.

# TODO: Update the project_country_abbrev_dict with the country_not_found_mapping dictionary
# HINT: This is relatively straightforward. Python dictionaries have a method called update(), which essentially
# appends a dictionary to another dictionary

project_country_abbrev_dict.update(country_not_found_mapping)

project_country_abbrev_dict

defaultdict(str,
            {'American Samoa': 'ASM',
             'Antigua and Barbuda': 'ATG',
             'Arab Republic of Egypt': 'EGY',
             'Argentine Republic': 'ARG',
             'Barbados': 'BRB',
             'Belize': 'BLZ',
             'Bosnia and Herzegovina': 'BIH',
             'Burkina Faso': 'BFA',
             'Central African Republic': 'CAF',
             'Commonwealth of Dominica': 'DMA',
             'Commonwealth of The Bahamas': 'BHS',
             'Czech Republic': 'CZE',
             'Democratic Republic of Timor-Leste': 'TLS',
             'Dominican Republic': 'DOM',
             'Federal Democratic Republic of Ethiopia': 'ETH',
             'Federal Republic of Nigeria': 'NGA',
             'Federated States of Micronesia': 'FSM',
             'Federative Republic of Brazil': 'BRA',
             'French Republic': 'FRA',
             'Gabonese Republic': 'GAB',
             'Georgia': 'GEO',
             'Grand Duchy of Luxembourg': 'LUX',
             'Grenada': 'GRD',
             'Hashemite Kingdom of Jordan': 'JOR',
             'Hellenic Republic': 'GRC',
             'Hungary': 'HUN',
             'Ireland': 'IRL',
             'Islamic Republic of Iran': 'IRN',
             'Islamic Republic of Mauritania': 'MRT',
             'Islamic Republic of Pakistan': 'PAK',
             'Italian Republic': 'ITA',
             'Jamaica': 'JAM',
             'Japan': 'JPN',
             'Kingdom of Belgium': 'BEL',
             'Kingdom of Bhutan': 'BTN',
             'Kingdom of Cambodia': 'KHM',
             'Kingdom of Denmark': 'DNK',
             'Kingdom of Lesotho': 'LSO',
             'Kingdom of Morocco': 'MAR',
             'Kingdom of Norway': 'NOR',
             'Kingdom of Spain': 'ESP',
             'Kingdom of Thailand': 'THA',
             'Kingdom of Tonga': 'TON',
             'Kingdom of the Netherlands': 'NLD',
             'Kyrgyz Republic': 'KGZ',
             "Lao People's Democratic Republic": 'LAO',
             'Lebanese Republic': 'LBN',
             'Malaysia': 'MYS',
             'Mongolia': 'MNG',
             'Montenegro': 'MNE',
             'Nepal': 'NPL',
             'New Zealand': 'NZL',
             "People's Democratic Republic of Algeria": 'DZA',
             "People's Republic of Bangladesh": 'BGD',
             "People's Republic of China": 'CHN',
             'Plurinational State of Bolivia': 'BOL',
             'Portuguese Republic': 'PRT',
             'Republic of Albania': 'ALB',
             'Republic of Angola': 'AGO',
             'Republic of Armenia': 'ARM',
             'Republic of Austria': 'AUT',
             'Republic of Azerbaijan': 'AZE',
             'Republic of Belarus': 'BLR',
             'Republic of Benin': 'BEN',
             'Republic of Botswana': 'BWA',
             'Republic of Bulgaria': 'BGR',
             'Republic of Burundi': 'BDI',
             'Republic of Cabo Verde': 'CPV',
             'Republic of Cameroon': 'CMR',
             'Republic of Chad': 'TCD',
             'Republic of Chile': 'CHL',
             'Republic of Colombia': 'COL',
             'Republic of Costa Rica': 'CRI',
             'Republic of Croatia': 'HRV',
             'Republic of Cyprus': 'CYP',
             'Republic of Djibouti': 'DJI',
             'Republic of Ecuador': 'ECU',
             'Republic of El Salvador': 'SLV',
             'Republic of Equatorial Guinea': 'GNQ',
             'Republic of Estonia': 'EST',
             'Republic of Fiji': 'FJI',
             'Republic of Finland': 'FIN',
             'Republic of Ghana': 'GHA',
             'Republic of Guatemala': 'GTM',
             'Republic of Guinea': 'GIN',
             'Republic of Guinea-Bissau': 'GNB',
             'Republic of Haiti': 'HTI',
             'Republic of Honduras': 'HND',
             'Republic of Iceland': 'ISL',
             'Republic of India': 'IND',
             'Republic of Indonesia': 'IDN',
             'Republic of Iraq': 'IRQ',
             'Republic of Kazakhstan': 'KAZ',
             'Republic of Kenya': 'KEN',
             'Republic of Kiribati': 'KIR',
             'Republic of Latvia': 'LVA',
             'Republic of Liberia': 'LBR',
             'Republic of Lithuania': 'LTU',
             'Republic of Madagascar': 'MDG',
             'Republic of Malawi': 'MWI',
             'Republic of Maldives': 'MDV',
             'Republic of Mali': 'MLI',
             'Republic of Malta': 'MLT',
             'Republic of Mauritius': 'MUS',
             'Republic of Moldova': 'MDA',
             'Republic of Mozambique': 'MOZ',
             'Republic of Namibia': 'NAM',
             'Republic of Nicaragua': 'NIC',
             'Republic of Palau': 'PLW',
             'Republic of Panama': 'PAN',
             'Republic of Paraguay': 'PRY',
             'Republic of Peru': 'PER',
             'Republic of Poland': 'POL',
             'Republic of Senegal': 'SEN',
             'Republic of Serbia': 'SRB',
             'Republic of Seychelles': 'SYC',
             'Republic of Sierra Leone': 'SLE',
             'Republic of Singapore': 'SGP',
             'Republic of Slovenia': 'SVN',
             'Republic of South Africa': 'ZAF',
             'Republic of South Sudan': 'SSD',
             'Republic of Suriname': 'SUR',
             'Republic of Tajikistan': 'TJK',
             'Republic of The Gambia': 'GMB',
             'Republic of Trinidad and Tobago': 'TTO',
             'Republic of Tunisia': 'TUN',
             'Republic of Turkey': 'TUR',
             'Republic of Uganda': 'UGA',
             'Republic of Uzbekistan': 'UZB',
             'Republic of Vanuatu': 'VUT',
             'Republic of Yemen': 'YEM',
             'Republic of Zambia': 'ZMB',
             'Republic of Zimbabwe': 'ZWE',
             'Republic of the Marshall Islands': 'MHL',
             'Republic of the Philippines': 'PHL',
             'Republic of the Sudan': 'SDN',
             'Romania': 'ROU',
             'Russian Federation': 'RUS',
             'Samoa': 'WSM',
             'Slovak Republic': 'SVK',
             'Solomon Islands': 'SLB',
             'State of Israel': 'ISR',
             'Sultanate of Oman': 'OMN',
             'Syrian Arab Republic': 'SYR',
             'Taiwan': 'TWN',
             'Turkmenistan': 'TKM',
             'Tuvalu': 'TUV',
             'Ukraine': 'UKR',
             'Union of the Comoros': 'COM',
             'United Mexican States': 'MEX',
             'United Republic of Tanzania': 'TZA',
             'Co-operative Republic of Guyana': 'GUY',
             'Commonwealth of Australia': 'AUS',
             'Democratic Republic of Sao Tome and Prin': 'STP',
             'Democratic Republic of the Congo': 'COD',
             'Democratic Socialist Republic of Sri Lan': 'LKA',
             'East Asia and Pacific': 'EAS',
             'Europe and Central Asia': 'ECS',
             'Islamic  Republic of Afghanistan': 'AFG',
             'Latin America': 'LCN',
             'Caribbean': 'LCN',
             'Macedonia': 'MKD',
             'Middle East and North Africa': 'MEA',
             'Oriental Republic of Uruguay': 'URY',
             'Republic of Congo': 'COG',
             "Republic of Cote d'Ivoire": 'CIV',
             'Republic of Korea': 'KOR',
             'Republic of Niger': 'NER',
             'Republic of Kosovo': 'XKX',
             'Republic of Rwanda': 'RWA',
             'Republic of Togo': 'TGO',
             'Republic of the Union of Myanmar': 'MMR',
             'Republica Bolivariana de Venezuela': 'VEN',
             'Sint Maarten': 'SXM',
             "Socialist People's Libyan Arab Jamahiriy": 'LBY',
             'Socialist Republic of Vietnam': 'VNM',
             'Somali Democratic Republic': 'SOM',
             'South Asia': 'SAS',
             'St. Kitts and Nevis': 'KNA',
             'St. Lucia': 'LCA',
             'St. Vincent and the Grenadines': 'VCT',
             'State of Eritrea': 'ERI',
             'The Independent State of Papua New Guine': 'PNG',
             'West Bank and Gaza': 'PSE',
             'World': 'WLD'})

Part 5 - Make a 'Country Code' Column¶

Next, create a 'Country Code' column in the data_projects data frame. Use the project_country_abbrev_dict and df_projects['Country Name'] column to create a new columns called 'Country 'Code'.

# TODO: Use the project_country_abbrev_dict and the df_projects['Country Name'] column to make a new column
# of the alpha-3 country codes. This new column should be called 'Country Code'.

# HINT: Use the apply method and a lambda function
# HINT: The lambda function will use the project_country_abbrev_dict that maps the country name to the ISO code
# https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.apply.html

df_projects['Country Code'] = df_projects['Official Country Name'].apply(lambda x: project_country_abbrev_dict[x])

# Run this code cell to see which projects in the df_projects data frame still have no country code abbreviation.
# In other words, these projects do not have a matching population value in the df_indicator data frame.
df_projects[df_projects['Country Code'] == '']

You'll notice that there are still a few entries without country abbreviations. This includes projects that were labeled as "Africa" rather than a specific country. It also includes "Yugoslavia", which is a country that ceased to exist in the 1990s.

Conclusion¶

Now the df_projects dataframe and the df_indicator dataframe have a matching column called 'Country Code'. But these two data frames can't be merged quite yet.

Each project in the df_projects dataframe also has a date associated with it. The idea would be to merge the df_projects dataframe with the df_indicator dataframe so that each project also had a population value associated with it. There are still more data transformations to do in order for that to be possible.

In fact, the challenge problem from the previous exercise on merging data would help quite a bit. In that exercise, the indicator data was transformed from a wide format to a long format.

You could then merge the df_projects dataframe and the df_indicator dataframe using the alpha-3 country abbreviation and the project or indicator year. You can start to see how data transformations become a series of processes that pipeline data from one format into a different format.

Data Types¶

When reading in a data set, pandas will try to guess the data type of each column like float, integer, datettime, bool, etc. In Pandas, strings are called "object" dtypes.

However, Pandas does not always get this right. That was the issue with the World Bank projects data. Hence, the dtype was specified as a string:

df_projects = pd.read_csv('../data/projects_data.csv', dtype=str)

Run the code cells below to read in the indicator and projects data. Then run the following code cell to see the dtypes of the indicator data frame.

# Run this code cell

import pandas as pd

# read in the population data and drop the final column
df_indicator = pd.read_csv('population_data.csv', skiprows=4)
df_indicator.drop(['Unnamed: 62'], axis=1, inplace=True)

# read in the projects data set with all columns type string
df_projects = pd.read_csv('projects_data.csv', dtype=str)
df_projects.drop(['Unnamed: 56'], axis=1, inplace=True)

# Run this code cell 
df_indicator.dtypes

Country Name       object
Country Code       object
Indicator Name     object
Indicator Code     object
1960              float64
1961              float64
1962              float64
1963              float64
1964              float64
1965              float64
1966              float64
1967              float64
1968              float64
1969              float64
1970              float64
1971              float64
1972              float64
1973              float64
1974              float64
1975              float64
1976              float64
1977              float64
1978              float64
1979              float64
1980              float64
1981              float64
1982              float64
1983              float64
1984              float64
1985              float64
1986              float64
1987              float64
1988              float64
1989              float64
1990              float64
1991              float64
1992              float64
1993              float64
1994              float64
1995              float64
1996              float64
1997              float64
1998              float64
1999              float64
2000              float64
2001              float64
2002              float64
2003              float64
2004              float64
2005              float64
2006              float64
2007              float64
2008              float64
2009              float64
2010              float64
2011              float64
2012              float64
2013              float64
2014              float64
2015              float64
2016              float64
2017              float64
dtype: object

df_indicator.head()

These results look reasonable. Country Name, Country Code, Indicator Name and Indicator Code were all read in as strings. The year columns, which contain the population data, were read in as floats.

Exercise 1¶

Since the population indicator data was read in correctly, you can run calculations on the data. In this first exercise, sum the populations of the United States, Canada, and Mexico by year.

# TODO: Calculate the population sum by year for Canada,
#       the United States, and Mexico.

# the keepcol variable makes a list of the column names to keep. You can use this if you'd like
keepcol = ['Country Name']
for i in range(1960, 2018, 1):
    keepcol.append(str(i))

# TODO: In the df_nafta variable, store a data frame that only contains the rows for 
#      Canada, United States, and Mexico.
df_nafta = df_indicator[df_indicator['Country Code'].isin(['USA','CAN','MEX'])]


# TODO: Calculate the sum of the values in each column in order to find the total population by year.
# You can use the keepcol variable if you want to control which columns get outputted
df_nafta[keepcol].sum()

Country Name    CanadaMexicoUnited States
1960                          2.36754e+08
1961                          2.41356e+08
1962                          2.45802e+08
1963                          2.50146e+08
1964                          2.54478e+08
1965                          2.58604e+08
1966                          2.62619e+08
1967                          2.66554e+08
1968                          2.70344e+08
1969                          2.74128e+08
1970                          2.78406e+08
1971                          2.83025e+08
1972                          2.87368e+08
1973                          2.91559e+08
1974                          2.95716e+08
1975                          3.00054e+08
1976                          3.04177e+08
1977                          3.08381e+08
1978                           3.1266e+08
1979                          3.17042e+08
1980                          3.21179e+08
1981                          3.25358e+08
1982                          3.29469e+08
1983                          3.33445e+08
1984                          3.37308e+08
1985                          3.41227e+08
1986                          3.45271e+08
1987                          3.49342e+08
1988                          3.53478e+08
1989                          3.57896e+08
1990                          3.62772e+08
1991                          3.68224e+08
1992                          3.73862e+08
1993                          3.79353e+08
1994                          3.84587e+08
1995                          3.89678e+08
1996                          3.94753e+08
1997                          3.99926e+08
1998                          4.04923e+08
1999                           4.0984e+08
2000                          4.14652e+08
2001                          4.19118e+08
2002                          4.23343e+08
2003                          4.27424e+08
2004                          4.31796e+08
2005                          4.36301e+08
2006                          4.41043e+08
2007                          4.45955e+08
2008                          4.51002e+08
2009                          4.55905e+08
2010                          4.60663e+08
2011                          4.65077e+08
2012                          4.69572e+08
2013                          4.73923e+08
2014                          4.78379e+08
2015                          4.82763e+08
2016                          4.87211e+08
2017                          4.91591e+08
dtype: object

df_nafta

Exercise 2¶

Now, run the code cell below to look at the dtypes for the projects data set. They should all be "object" types, ie strings, because that's what was specified in the code when reading in the csv file. As a reminder, this was the code:

df_projects = pd.read_csv('../data/projects_data.csv', dtype=str)

# Run this code cell
df_projects.dtypes

id                          object
regionname                  object
countryname                 object
prodline                    object
lendinginstr                object
lendinginstrtype            object
envassesmentcategorycode    object
supplementprojectflg        object
productlinetype             object
projectstatusdisplay        object
status                      object
project_name                object
boardapprovaldate           object
board_approval_month        object
closingdate                 object
lendprojectcost             object
ibrdcommamt                 object
idacommamt                  object
totalamt                    object
grantamt                    object
borrower                    object
impagency                   object
url                         object
projectdoc                  object
majorsector_percent         object
sector1                     object
sector2                     object
sector3                     object
sector4                     object
sector5                     object
sector                      object
mjsector1                   object
mjsector2                   object
mjsector3                   object
mjsector4                   object
mjsector5                   object
mjsector                    object
theme1                      object
theme2                      object
theme3                      object
theme4                      object
theme5                      object
theme                       object
goal                        object
financier                   object
mjtheme1name                object
mjtheme2name                object
mjtheme3name                object
mjtheme4name                object
mjtheme5name                object
location                    object
GeoLocID                    object
GeoLocName                  object
Latitude                    object
Longitude                   object
Country                     object
dtype: object

Many of these columns should be strings, so there's no problem; however, a few columns should be other data types. For example, boardapprovaldate should be a datettime and totalamt should be an integer. You'll learn about datetime formatting in the next part of the lesson. For this exercise, focus on the 'totalamt' and 'lendprojectcost' columns. Run the code cell below to see what that data looks like

# Run this code cell
df_projects[['totalamt', 'lendprojectcost']].head()

# Run this code cell to take the sum of the total amount column
df_projects['totalamt'].sum()

'0200,000,00058,330,00020,000,000100,000,000500,000,000350,000,000225,000,000125,000,000329,900,000200,000,000400,000,0000150,000,0000389,000,0000530,000,000350,000,000202,000,000300,000,000500,000,000493,060,000175,000,0000230,000,00080,000,000400,000,000130,000,00030,000,00035,000,0000250,000,000200,000,000400,000,000110,000,000150,000,00074,000,000118,000,000465,000,000050,000,00090,000,000100,000,000130,000,00010,000,000460,600,000100,000,000700,000,00030,000,000280,000,00040,000,000300,000,00090,000,000150,000,000150,000,00043,000,000100,000,00015,000,00015,000,000012,000,00030,000,000100,000,000250,000,000200,000,000150,000,00025,000,000500,000,000122,100,00020,000,00007,390,000000050,000,00075,000,00060,000,00025,000,00015,000,00000300,000,00025,000,00011,000,0009,000,000130,000,000140,000,000100,000,000200,000,00070,000,000140,000,00020,000,000188,000,000200,000,000500,000,00014,200,00040,000,000150,000,000003,550,000600,000,000400,000,0000150,000,0006,000,00012,000,000100,000,000300,000,00041,000,00040,000,00015,000,00075,000,0000220,000,00080,000,000360,000,000220,000,0000150,000,000120,000,00035,000,00011,000,000021,700,000130,000,00048,000,00060,000,00036,000,00041,000,00025,000,000180,000,00090,000,000100,000,000250,000,000100,000,00020,000,00015,000,00045,000,00050,000,0000500,000,00050,000,00025,000,00040,000,00091,540,00025,000,000030,000,000200,000,000050,000,00010,000,00055,000,000190,000,000000025,000,000200,000,0000015,000,00015,000,0000030,000,000200,000,00050,000,00015,000,000450,640,00055,000,000110,000,00050,000,000145,000,000110,000,00031,100,000160,000,00051,000,00050,000,000140,000,00040,000,00030,000,000050,000,00066,000,00050,000,000200,000,000200,000,000027,800,00025,000,00020,000,00080,000,000225,200,00050,000,00060,000,000250,000,00035,000,000210,000,000600,000,00048,000,00010,000,00014,000,000000375,000,00015,000,000060,000,00045,000,00025,000,000420,000,00000026,750,00010,000,000010,000,0000486,000,00015,000,0000000300,000,00000300,000,000210,000,000110,000,000500,000,00034,890,000200,000,0001,800,00015,000,000300,000,00056,000,0000140,000,00020,000,00050,000,000020,000,0000170,000,00000120,000,00000040,000,000200,000,000200,000,00051,900,00035,000,00026,000,00080,000,000200,000,000100,000,0002,800,00093,000,000425,000,000400,000,00025,000,000125,000,000300,000,000510,000,00080,000,000150,000,00015,000,00034,000,000300,000,000300,000,000245,000,000041,900,0000500,000,000300,000,00075,000,000250,000,000200,000,000170,000,00080,000,000080,000,00060,600,00045,000,0000170,000,00000147,700,000125,000,000120,000,0001,150,000,000100,000,0000318,000,000125,000,000130,000,000120,000,00042,000,00010,000,000040,000,000020,000,0005,000,0000031,000,00018,300,000122,380,0008,750,00045,000,00002,000,0000020,000,0000200,000,000118,610,000150,000,00028,000,000120,000,0000300,000,000400,000,0005,000,00046,000,0000000050,000,00050,130,0000160,000,00000015,000,0005,000,000010,000,00060,000,00016,000,0005,000,000150,000,00060,000,000207,600,000150,000,000150,000,0000155,000,0007,000,00010,000,000000114,000,000000200,000,000700,000,000600,000,00060,000,0000150,000,00000200,000,000119,000,00060,000,0000400,000,000200,000,000020,000,000100,000,00050,000,000160,000,00000000060,000,00082,500,00012,000,00015,700,00050,000,000055,000,000500,000,0000000300,000,000150,000,000070,000,00003,000,0000060,000,00070,000,0000100,000,0000050,000,000025,000,00030,000,00020,000,00030,000,00062,000,00050,000,000120,000,000144,900,000345,000,000200,000,00030,000,00060,000,00045,000,00056,000,000020,800,000065,000,0000153,000,0000040,000,00045,700,00030,000,000200,000,00026,500,00012,000,00095,800,000150,000,000250,000,00017,500,00060,000,00047,490,000050,000,00050,000,000150,000,000150,000,00036,000,00048,400,000611,000,00040,000,000033,630,000350,000,00010,000,00010,000,000042,000,00035,000,000137,000,000116,000,00027,000,00086,000,000100,000,00035,000,00020,000,00018,000,00025,000,00020,000,0000500,000,000100,000,00020,300,00060,000,00036,100,000127,700,00099,310,00025,200,00022,000,00020,000,00053,000,00023,000,00012,000,00006,200,000080,000,00080,000,000020,000,00070,000,00049,850,00010,000,000150,000,000072,400,000120,000,00023,300,00059,000,0000100,000,000125,000,00020,000,00038,590,0004,000,00015,000,00016,200,00050,000,000240,000,00025,000,000118,000,00050,000,00039,200,000240,000,000100,000,00072,520,00029,000,00061,620,00010,700,000300,000,0008,300,00081,000,00095,000,00080,000,00017,000,000200,000,00083,000,00015,270,00025,000,00036,000,000350,000,00045,000,00028,000,000155,000,0000097,000,0000100,000,0000026,300,0000600,000,00019,630,000150,000,000200,000,0004,000,000040,000,000236,200,00078,740,00075,000,00080,000,000145,000,00090,000,0000018,000,000200,000,00040,000,00017,000,00050,000,000225,700,00070,000,000150,000,000108,100,00040,000,00073,300,00023,330,00050,000,00060,000,00022,000,00013,330,0000370,700,000200,000,000025,000,0005,000,000035,200,000074,000,000300,000,00059,000,000103,000,00050,000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What just happened? Pandas treated the totalamts like strings. In Python, adding strings concatenates the strings together.

There are a few ways to remedy this. When using pd.read_csv(), you could specify the column type for every column in the data set. The pd.read_csv() dtype option can accept a dictionary mapping each column name to its data type. You could also specify the thousands option with thousands=','. This specifies that thousands are separated by a comma in this data set.

However, this data is somewhat messy, contains missing values, and has a lot of columns. It might be faster to read in the entire data set with string types and then convert individual columns as needed. For this next exercise, convert the totalamt column from a string to an integer type.

# TODO: Convert the totalamt column from a string to a float and save the results back into the totalamt column

# Step 1: Remove the commas from the 'totalamt' column
# HINT: https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.Series.str.replace.html
df_projects['totalamt'] = df_projects['totalamt'].apply(lambda x: ''.join(x.split(',')))
# Step 2: Convert the 'totalamt' column from an object data type (ie string) to an integer data type.
# HINT: https://pandas.pydata.org/pandas-docs/version/0.23/generated/pandas.to_numeric.html
df_projects['totalamt'] = df_projects['totalamt'].astype(int)

Conclusion¶

With messy data, you might find it easier to read in everything as a string; however, you'll sometimes have to convert those strings to more appropriate data types. When you output the dtypes of a dataframe, you'll generally see these values in the results:

float64
int64
bool
datetime64
timedelta
object

where timedelta is the difference between two datetimes and object is a string. As you've seen here, you sometimes need to convert data types from one type to another type. Pandas has a few different methods for converting between data types, and here are link to the documentation:

Parsing Dates¶

Another common data transformation involves parsing dates. Parsing generally means that you start with a string and then transform that string into a different data type. In this case, that means taking a date in the format of a string and transforming the string into a date type. Run the next cell to see an example.

import pandas as pd
parsed_date = pd.to_datetime('January 1st, 2017')
parsed_date

Timestamp('2017-01-01 00:00:00')

parsed_date.month

1

parsed_date.year

2017

parsed_date.second

0

Sometimes date string are formatted in unexpected ways. For example, in the United States, dates are given with the month first and then the day. That is what pandas expects by default. However, some countries write the date with the day first and then the month. Run the next three examples to see Panda's default behavior and how you can specify the date formatting.

parsed_date = pd.to_datetime('5/3/2017 5:30')
parsed_date.month

5

parsed_date = pd.to_datetime('3/5/2017 5:30', format='%d/%m/%Y %H:%M')
parsed_date.month

5

parsed_date = pd.to_datetime('5/3/2017 5:30', format='%m/%d/%Y %H:%M')
parsed_date.month

5

The formatting abbreviations are actually part of the python standard. You can see examples at this link.

Part 1 - Practice Parsing Dates¶

Run the code cells below to import the World Bank projects data. The last line of the code outputs all of the column names in the data frame.

# Run this code cell. Read in the projects data set with all columns type string
df_projects = pd.read_csv('projects_data.csv', dtype=str)
df_projects.drop(['Unnamed: 56'], axis=1, inplace=True)
df_projects.columns

Index(['id', 'regionname', 'countryname', 'prodline', 'lendinginstr',
       'lendinginstrtype', 'envassesmentcategorycode', 'supplementprojectflg',
       'productlinetype', 'projectstatusdisplay', 'status', 'project_name',
       'boardapprovaldate', 'board_approval_month', 'closingdate',
       'lendprojectcost', 'ibrdcommamt', 'idacommamt', 'totalamt', 'grantamt',
       'borrower', 'impagency', 'url', 'projectdoc ', 'majorsector_percent ',
       'sector1', 'sector2', 'sector3', 'sector4', 'sector5', 'sector',
       'mjsector1', 'mjsector2', 'mjsector3', 'mjsector4', 'mjsector5',
       'mjsector', 'theme1', 'theme2', 'theme3', 'theme4', 'theme5', 'theme ',
       'goal', 'financier', 'mjtheme1name', 'mjtheme2name', 'mjtheme3name',
       'mjtheme4name', 'mjtheme5name', 'location', 'GeoLocID', 'GeoLocName',
       'Latitude', 'Longitude', 'Country'],
      dtype='object')

Notice there are three columns associated with dates: boardapprovaldate, board_approval_month, and closingdate. Run the code cell below to see what these values look like.

# Run this code cell
df_projects.head(15)[['boardapprovaldate', 'board_approval_month', 'closingdate']]

# Use the pandas to_datetime method to convert the boardapprovaldate and 
# closingdate columns into datetime objects.

# TODO: Use the pandas to_datetime method to convert these two columns 
#   (boardapprovaldate, closingdate) into date times.
# HINT: It's easier to do this one column at a time

df_projects['boardapprovaldate'] = pd.to_datetime(df_projects['boardapprovaldate'])
df_projects['closingdate'] = pd.to_datetime(df_projects['closingdate'])

# Run the code cells below to see how you can access the different parts of the datetime objects
# Series.dt gives access to the datetime object as explained here: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.dt.html
df_projects['boardapprovaldate'].dt.second

0        0.0
1        0.0
2        0.0
3        0.0
4        0.0
        ... 
18243    0.0
18244    0.0
18245    0.0
18246    0.0
18247    0.0
Name: boardapprovaldate, Length: 18248, dtype: float64

# Run this code cell to see the output
df_projects['boardapprovaldate'].dt.month

0        6.0
1        6.0
2        6.0
3        6.0
4        6.0
        ... 
18243    3.0
18244    8.0
18245    8.0
18246    8.0
18247    5.0
Name: boardapprovaldate, Length: 18248, dtype: float64

# Run this code to see the output
# weekday represents the day of the week from 0 (Monday) to 6 (Sunday).
df_projects['boardapprovaldate'].dt.weekday

0        3.0
1        3.0
2        3.0
3        2.0
4        2.0
        ... 
18243    3.0
18244    3.0
18245    4.0
18246    3.0
18247    4.0
Name: boardapprovaldate, Length: 18248, dtype: float64

Part 2 - Create new columns¶

Now that the boardapprovaldate and closingdates are in datetime formats, create a few new columns in the df_projects data frame:

approvalyear
approvalday
approvalweekday
closingyear
closingday
closingweekday

### 
# TODO create the follwing new columns in the df_projects data frame
#
# approvalyear
# approvalday
# approvalweekday
# closingyear
# closingday
# closingweekday
#
#
###

df_projects['approvalyear'] = df_projects['boardapprovaldate'].dt.year
df_projects['approvalday'] = df_projects['boardapprovaldate'].dt.day
df_projects['approvalweekday'] = df_projects['boardapprovaldate'].dt.weekday
df_projects['closingyear'] = df_projects['closingdate'].dt.year
df_projects['closingday'] = df_projects['closingdate'].dt.day
df_projects['closingweekday'] = df_projects['closingdate'].dt.weekday

Part 3 (Challenge)¶

Use what you've practiced in this exercise to make a visualization of year on the x-axis and the sum of the totalamt columns per year on the y-axis.

You'll first need to clean the totalamt column to get rid of commas and convert the values to numeric. Then you'll need to use pandas' groupby method to sum the totalamt column for each year.

Finally, you can use the pandas plot() method to output the visualization.

###
# TODO: Make a visualization with year on the x-axis and the sum of the totalamt columns per year on the y-axis
# HINT: The totalamt column is currently a string with commas. For example 100,250,364. You'll need to remove the
#         commas and convert the column to a numeric variable.
# HINT: pandas groupby, sum, and plot methods should also be helpful
####

import matplotlib.pyplot as plt
%matplotlib inline

# TODO: Step 1 - convert the totalamt column from string to numeric. Be sure to remove the commas in this column
df_projects['totalamt'] = df_projects['totalamt'].apply(lambda x : ''.join(x.split(',')))
df_projects['totalamt'] = df_projects['totalamt'].astype(int)

# df_projects['totalamt'] = pd.to_numeric(df_projects['totalamt'].str.replace(',',''))

# TODO: Step 2 - calculate the sum of the totalamt column by year and plot the results with a line plot
df_projects.groupby('approvalyear').totalamt.sum().plot(kind = 'line', figsize = (12,8))
plt.xlabel('year')
plt.ylabel('amount $')
plt.show();

<matplotlib.axes._subplots.AxesSubplot at 0x12a377e90>

Conclusion¶

Parsing dates is a common task data transformation task. This is true not just with pandas but with other data systems like SQL.

Encodings¶

Encodings are a set of rules mapping string characters to their binary representations. Python supports dozens of different encoding as seen here in this link. Because the web was originally in English, the first encoding rules mapped binary code to the English alphabet.

The English alphabet has only 26 letters. But other languages have many more characters including accents, tildes and umlauts. As time went on, more encodings were invented to deal with languages other than English. The utf-8 standard tries to provide a single encoding schema that can encompass all text.

The problem is that it's difficult to know what encoding rules were used to make a file unless somebody tells you. The most common encoding by far is utf-8. Pandas will assume that files are utf-8 when you read them in or write them out.

Run the code cell below to read in the population data set.

import pandas as pd
df = pd.read_csv('population_data.csv', skiprows=4)

import pandas as pd
df = pd.read_csv('mystery.csv')

---------------------------------------------------------------------------
UnicodeDecodeError                        Traceback (most recent call last)
<ipython-input-41-46474c2a2e94> in <module>
      1 import pandas as pd
----> 2 df = pd.read_csv('mystery.csv')

~/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in parser_f(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
    674         )
    675 
--> 676         return _read(filepath_or_buffer, kwds)
    677 
    678     parser_f.__name__ = name

~/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
    446 
    447     # Create the parser.
--> 448     parser = TextFileReader(fp_or_buf, **kwds)
    449 
    450     if chunksize or iterator:

~/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
    878             self.options["has_index_names"] = kwds["has_index_names"]
    879 
--> 880         self._make_engine(self.engine)
    881 
    882     def close(self):

~/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
   1112     def _make_engine(self, engine="c"):
   1113         if engine == "c":
-> 1114             self._engine = CParserWrapper(self.f, **self.options)
   1115         else:
   1116             if engine == "python":

~/opt/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
   1889         kwds["usecols"] = self.usecols
   1890 
-> 1891         self._reader = parsers.TextReader(src, **kwds)
   1892         self.unnamed_cols = self._reader.unnamed_cols
   1893 

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.__cinit__()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._get_header()

UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte

You should have gotten an error: UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte. This means pandas assumed the file had a utf-8 encoding but had trouble reading in the data file.

Your job in the next cell is to figure out the encoding for the mystery.csv file.

# TODO: Figure out what the encoding is of the myster.csv file
# HINT: pd.read_csv('mystery.csv', encoding=?) where ? is the string for an encoding like 'ascii'
# HINT: This link has a list of encodings that Python recognizes https://docs.python.org/3/library/codecs.html#standard-encodings

# Python has a file containing a dictionary of encoding names and associated aliases
# This line imports the dictionary and then creates a set of all available encodings
# You can use this set of encodings to search for the correct encoding
# If you'd like to see what this file looks like, execute the following Python code to see where the file is located
#    from encodings import aliases
#    aliases.__file__

from encodings.aliases import aliases

alias_values = set(aliases.values())

# TODO: iterate through the alias_values list trying out the different encodings to see which one or ones work
# HINT: Use a try - except statement. Otherwise your code will produce an error when reading in the csv file
#       with the wrong encoding.
# HINT: In the try statement, print out the encoding name so that you know which one(s) worked.
fit_list = []
for i in alias_values:

    try:
        df = pd.read_csv('mystery.csv', encoding = i)
        fit_list.append(i)
    except:
        continue

fit_list

['cp1140',
 'utf_16_le',
 'cp273',
 'utf_16_be',
 'cp037',
 'utf_16',
 'cp1026',
 'cp500']

from encodings import aliases
aliases.__file__

'/Users/xuhao3/opt/anaconda3/lib/python3.7/encodings/aliases.py'

Conclusion¶

There are dozens of encodings that Python can handle; however, Pandas assumes a utf-8 encoding. This makes sense since utf-8 is very common. However, you will sometimes come across files with other encodings. If you don't know the encoding, you have to search for it.

Note, as always, there is a solution file for this exercise. Go to File->Open.

There is a Python library that can be of some help when you don't know an encoding: chardet. Run the code cells below to see how it works.

# install the chardet library
!pip install chardet

Requirement already satisfied: chardet in /Users/xuhao3/opt/anaconda3/lib/python3.7/site-packages (3.0.4)

# import the chardet library
import chardet 

# use the detect method to find the encoding
# 'rb' means read in the file as binary
with open("mystery.csv", 'rb') as file:
    print(chardet.detect(file.read()))

{'encoding': 'UTF-16', 'confidence': 1.0, 'language': ''}

Imputing Data¶

When a dataset has missing values, you can either remove those values or fill them in. In this exercise, you'll work with World Bank GDP (Gross Domestic Product) data to fill in missing values.

# run this code cell to read in the data set
import pandas as pd
df = pd.read_csv('gdp_data.csv', skiprows=4)
df.drop('Unnamed: 62', axis=1, inplace=True)

# run this code cell to see what the data looks like
df.head()

# Run this code cell to check how many null values are in the data set
df.isnull().sum()

Country Name        0
Country Code        0
Indicator Name      0
Indicator Code      0
1960              140
                 ... 
2013               16
2014               17
2015               18
2016               23
2017               30
Length: 62, dtype: int64

import matplotlib.pyplot as plt
%matplotlib inline

# put the data set into long form instead of wide
df_melt = pd.melt(df, id_vars=['Country Name', 'Country Code', 'Indicator Name', 'Indicator Code'], var_name='year', value_name='GDP')

# convert year to a date time
df_melt['year'] = pd.to_datetime(df_melt['year'])

def plot_results(column_name):
    # plot the results for Afghanistan, Albania, and Honduras
    fig, ax = plt.subplots(figsize=(8,6))

    df_melt[(df_melt['Country Name'] == 'Afghanistan') | 
            (df_melt['Country Name'] == 'Albania') | 
            (df_melt['Country Name'] == 'Honduras')].groupby('Country Name').plot('year', column_name, legend=True, ax=ax)
    ax.legend(labels=['Afghanistan', 'Albania', 'Honduras'])
    
plot_results('GDP')

Afghanistan and Albania are missing data, which show up as gaps in the results.

Exercise - Part 1¶

Your first task is to calculate mean GDP for each country and fill in missing values with the country mean. This is a bit tricky to do in pandas. Here are a few links that should be helpful:

df_melt.head(1)

# TODO: Use the df_melt dataframe and fill in missing values with a country's mean GDP
# If you aren't sure how to do this, 
# look up something like "how to group data and fill in nan values in pandas" in a search engine
# Put the results in a new column called 'GDP_filled'.

# HINT: You can do this with these methods: groupby(), transform(), a lambda function, fillna(), and mean()

df_melt['GDP_filled'] = df_melt.groupby('Country Name')['GDP'].\
                            apply(lambda col: col.fillna(col.mean()))

plot_results('GDP_filled')

This is somewhat of an improvement. At least there is no missing data; however, because GDP tends to increase over time, the mean GDP is probably not the best way to fill in missing values for this particular case. Next, try using forward fill to deal with any missing values.

Excercise - Part 2¶

Use the fillna forward fill method to fill in the missing data. Here is the documentation. As explained in the course video, forward fill takes previous values to fill in nulls.

The pandas fillna method has a forward fill option. For example, if you wanted to use forward fill on the GDP dataset, you could execute df_melt['GDP'].fillna(method='ffill'). However, there are two issues with that code.

You want to first make sure the data is sorted by year
You need to group the data by country name so that the forward fill stays within each country

Write code to first sort the df_melt dataframe by year, then group by 'Country Name', and finally use the forward fill method.

# TODO: Use forward fill to fill in missing GDP values
# HINTS: use the sort_values(), groupby(), and fillna() methods
df_melt['GDP_ffill'] = df_melt.sort_values('year').groupby('Country Name')['GDP'].fillna(method = 'ffill')

# plot the results
plot_results('GDP_ffill')

This looks better at least for the Afghanistan data; however, the Albania data is still missing values. You can fill in the Albania data using back fill. That is what you'll do next.

Exercise - Part 3¶

This part is similar to Part 2, but now you will use backfill. Write code that backfills the missing GDP data.

# TODO: Use back fill to fill in missing GDP values
# HINTS: use the sort_values(), groupby(), and fillna() methods
df_melt['GDP_bfill'] = df_melt.sort_values('year').groupby('Country Name')['GDP'].fillna(method = 'bfill')

# plot the results
plot_results('GDP_bfill')

Conclusion¶

In this case, the GDP data for all three countries is now complete. Note that forward fill did not fill all the Albania data because the first data entry in 1960 was NaN. Forward fill would try to fill the 1961 value with the NaN value from 1960.

To completely fill the entire GDP data for all countries, you might have to run both forward fill and back fill. Note as well that the results will be slightly different depending on if you run forward fill first or back fill first. Afghanistan, for example, is missing data in the middle of the data set. Hence forward fill and back fill will have slightly different results.

Run this next code cell to see if running both forward fill and back fill end up filling all the GDP NaN values.

# Run forward fill and backward fill on the GDP data
df_melt['GDP_ff_bf'] = df_melt.sort_values('year').groupby('Country Name')['GDP'].fillna(method='ffill').fillna(method='bfill')

# Check if any GDP values are null
df_melt['GDP_ff_bf'].isnull().sum()

0

Duplicate Data¶

A data set might have duplicate data: in other words, the same record is represented multiple times. Sometimes, it's easy to find and eliminate duplicate data like when two records are exactly the same. At other times, like what was discussed in the video, duplicate data is hard to spot.

Exercise 1¶

From the World Bank GDP data, count the number of countries that have had a project totalamt greater than 1 billion dollars (1,000,000,000). To get the count, you'll have to remove duplicate data rows.

import pandas as pd

# read in the projects data set and do some basic wrangling 
projects = pd.read_csv('projects_data.csv', dtype=str)
projects.drop('Unnamed: 56', axis=1, inplace=True)
projects['totalamt'] = pd.to_numeric(projects['totalamt'].str.replace(',', ''))
projects['countryname'] = projects['countryname'].str.split(';', expand=True)[0]
projects['boardapprovaldate'] = pd.to_datetime(projects['boardapprovaldate'])

# TODO: filter the data frame for projects over 1 billion dollars
projects[projects['totalamt'] > 1000000000].id.nunique()


# TODO: count the number of unique countries in the results

44

projects.head()

Exercise 2 (challenge)¶

This exercise is more challenging. The projects data set contains data about Yugoslavia, which was an Eastern European country until 1992. Yugoslavia eventually broke up into 7 countries: Bosnia and Herzegovina, Croatia, Kosovo, Macedonia, Montenegro, Serbia, and Slovenia.

But the projects dataset has some ambiguity in how it treats Yugoslavia and the 7 countries that came from Yugoslavia. Your task is to find Yugoslavia projects that are probably represented multiple times in the data set.

# TODO: output all projects for the 'Socialist Federal Republic of Yugoslavia'
# HINT: You can use the exact country name or use the pandas str.contains() method 
# to search for Yugoslavia

projects[projects['countryname'].str.contains('Yugoslavia')]

Yugoslavia officially ended on April 27th, 1992.

In the code cell below, filter for projects with a 'boardapprovaldate' prior to April 27th, 1992 and with 'countryname' Bosnia and Herzegovina, Croatia, Kosovo, Macedonia, Serbia or Slovenia. You'll see there are a total of 12 projects in the data set that match this criteria. Save the results in the republics variable

import datetime

# TODO: filter the projects data set for project boardapprovaldate prior to April 27th, 1992 AND with countryname
#  of either 'Bosnia and Herzegovina', 'Croatia', 'Kosovo', 'Macedonia', 'Serbia', or 'Sovenia'. Store the
#  results in the republics variable

republics = projects[(projects['boardapprovaldate'] < datetime.datetime(1992,4,27,tzinfo=datetime.timezone.utc)) &
                   (projects['countryname'].str.contains('Bosnia') |
                    projects['countryname'].str.contains('Croatia') |
                    projects['countryname'].str.contains('Kosovo') |
                    projects['countryname'].str.contains('Macedonia') |
                    projects['countryname'].str.contains('Serbia') |
                    projects['countryname'].str.contains('Sovenia') |
                    projects['countryname'].str.contains('Montenegro'))]\
                        [['regionname', 'countryname', 'lendinginstr', 'totalamt', 
                          'boardapprovaldate','location','GeoLocID', 'GeoLocName', 
                          'Latitude','Longitude','Country', 'project_name']].sort_values('boardapprovaldate')



#  TODO: so that it's easier to see all the data, keep only these columns:
# ['regionname', 'countryname', 'lendinginstr', 'totalamt', 'boardapprovaldate',
# 'location','GeoLocID', 'GeoLocName', 'Latitude','Longitude','Country', 'project_name']

# TODO: sort the results by boardapprovaldate


# show the results
republics

Are these projects also represented in the data labeled Yugoslavia? In the code cell below, filter for Yugoslavia projects approved between February 1st, 1980 and May 23rd, 1989 which are the minimum and maximum dates in the results above. Store the results in the yugoslavia variable.

The goal is to see if there are any projects represented more than once in the data set.

# TODO: Filter the projects data for Yugoslavia projects between
# February 1st, 1980 and May 23rd, 1989. Store the results in the
# Yugoslavia variable. Keep the same columns as the previous code cell.
# Sort the values by boardapprovaldate

yugoslavia = projects[(projects['boardapprovaldate'] >= datetime.datetime(1980,2,1,tzinfo=datetime.timezone.utc)) &
                      (projects['boardapprovaldate'] <= datetime.datetime(1989,5,23,tzinfo=datetime.timezone.utc)) &
                      (projects['countryname'].str.contains('Yugoslavia'))]\
                                [['regionname', 'countryname', 'lendinginstr', 'totalamt', 
                               'boardapprovaldate', 'location', 'GeoLocID', 'GeoLocName',
                               'Latitude', 'Longitude', 'Country', 'project_name']].sort_values('boardapprovaldate')

# show the results
yugoslavia

And as a final step, try to see if there are any projects in the republics variable and yugoslavia variable that could be the same project.

There are multiple ways to do that. As a suggestion, find unique dates in the republics variable. Then separately find unique dates in the yugoslavia variable. Concatenate (ie append) the results together. And then count the number of times each date occurs in this list. If a date occurs twice, that means the same boardapprovaldate appeared in both the Yugoslavia data as well as in the republics data.

You should find that there are four suspicious cases:

July 26th, 1983
March 31st, 1987
October 13th, 1987
May 23rd, 1989

import numpy as np

# TODO: find the unique dates in the republics variable
republic_unique_dates = republics['boardapprovaldate'].unique()

# TODO: find the unique dates in the yugoslavia variable
yugoslavia_unique_dates = yugoslavia['boardapprovaldate'].unique()

# TODO: make a list of the results appending one list to the other
dates = np.append(republic_unique_dates , yugoslavia_unique_dates)

# TODO: print out the dates that appeared twice in the results
unique_dates, count = np.unique(dates, return_counts=True)

for i in range(len(unique_dates)):
    if count[i] == 2:
        print(unique_dates[i])

1983-07-26 00:00:00+00:00
1987-03-31 00:00:00+00:00
1987-10-13 00:00:00+00:00
1989-05-23 00:00:00+00:00

Conclusion¶

On July 26th, 1983, for example, projects were approved for Bosnia and Herzegovina, Croatia, Macedonia, Slovenia, and Yugoslavia. The code below shows the projects for that date. You'll notice that Yugoslavia had two projects, one of which was called "Power Transmission Project (03) Energy Managem...". The projects in the other countries were all called "POWER TRANS.III".

This looks like a case of duplicate data. What you end up doing with this knowledge would depend on the context. For example, if you wanted to get a true count for the total number of projects in the data set, should all of these projects be counted as one project?

Run the code cell below to see the projects in question.

import datetime

# run this code cell to see the duplicate data
pd.concat([yugoslavia[yugoslavia['boardapprovaldate'] == datetime.datetime(1983, 7, 26,tzinfo=datetime.timezone.utc)], 
                      republics[republics['boardapprovaldate'] == datetime.datetime(1983, 7, 26,tzinfo=datetime.timezone.utc)]
          ])

Dummy Variables Exercise¶

In this exercise, you'll create dummy variables from the projects data set. The idea is to transform categorical data like this:

Project ID	Project Category
0	Energy
1	Transportation
2	Health
3	Employment

into new features that look like this:

Project ID	Energy	Transportation	Health	Employment
0	1	0	0	0
1	0	1	0	0
2	0	0	1	0
3	0	0	0	1

(Note if you were going to use this data with a model influenced by multicollinearity, you would want to eliminate one of the columns to avoid redundant information.)

The reasoning behind these transformations is that machine learning algorithms read in numbers not text. Text needs to be converted into numbers. You could assign a number to each category like 1, 2, 3, and 4. But a categorical variable has no inherent order, so you want to reflect this in your features.

Pandas makes it very easy to create dummy variables with the get_dummies method. In this exercise, you'll create dummy variables from the World Bank projects data; however, there's a caveat. The World Bank data is not particularly clean, so you'll need to explore and wrangle the data first.

You'll focus on the text values in the sector variables.

Run the code cells below to read in the World Bank projects data set and then to filter out the data for text variables.

import pandas as pd
import numpy as np

# read in the projects data set and do basic wrangling 
projects = pd.read_csv('projects_data.csv', dtype=str)
projects.drop('Unnamed: 56', axis=1, inplace=True)
projects['totalamt'] = pd.to_numeric(projects['totalamt'].str.replace(',', ''))
projects['countryname'] = projects['countryname'].str.split(';', expand=True)[0]
projects['boardapprovaldate'] = pd.to_datetime(projects['boardapprovaldate'])

# keep the project name, lending, sector and theme data
sector = projects.copy()
sector = sector[['project_name', 'lendinginstr', 'sector1', 'sector2', 'sector3', 'sector4', 'sector5', 'sector',
          'mjsector1', 'mjsector2', 'mjsector3', 'mjsector4', 'mjsector5',
          'mjsector', 'theme1', 'theme2', 'theme3', 'theme4', 'theme5', 'theme ',
          'goal', 'financier', 'mjtheme1name', 'mjtheme2name', 'mjtheme3name',
          'mjtheme4name', 'mjtheme5name']]

Run the code cell below. This cell shows the percentage of each variable that is null. Notice the mjsector1 through mjsector5 variables are all null. The mjtheme1name through mjtheme5name are also all null as well as the theme variable.

Because these variables contain so many null values, they're probably not very useful.

# output percentage of values that are missing
100 * sector.isnull().sum() / sector.shape[0]

project_name      0.000000
lendinginstr      1.348093
sector1           0.000000
sector2          47.791539
sector3          64.450899
sector4          76.019290
sector5          85.132617
sector            3.496274
mjsector1       100.000000
mjsector2       100.000000
mjsector3       100.000000
mjsector4       100.000000
mjsector5       100.000000
mjsector          3.496274
theme1            0.000000
theme2           46.005042
theme3           58.987286
theme4           71.317405
theme5           83.954406
theme           100.000000
goal             33.510522
financier        61.310829
mjtheme1name    100.000000
mjtheme2name    100.000000
mjtheme3name    100.000000
mjtheme4name    100.000000
mjtheme5name    100.000000
dtype: float64

Exercise 1¶

The sector1 variable looks promising; it doesn't contain any null values at all. In the next cell, store the unique sector1 values in a list and output the results. Use the sort_values() and unique() methods.

# TODO: Create a list of the unique values in sector1. Use the sort_values() and unique() pandas methods. 
# And then convert those results into a Python list
uniquesectors1 = list(sector['sector1'].value_counts().index)
uniquesectors1

['!$!0',
 '(Historic)Highways!$!100!$!TH',
 'Central Government (Central Agencies)!$!100!$!BC',
 '(Historic)Agriculture adjustment!$!100!$!AA',
 'Irrigation and Drainage!$!100!$!AI',
 '(Historic)Financial sector development!$!100!$!FS',
 'Railways!$!100!$!TW',
 'Ports/Waterways!$!100!$!TP',
 '(Historic)Urban water supply!$!100!$!WU',
 'Forestry!$!100!$!AT',
 '(Historic)Hydro!$!100!$!PH',
 'Social Protection!$!100!$!SA',
 'Health!$!100!$!HG',
 'Other Public Administration!$!100!$!BZ',
 '(Historic)Economic management!$!100!$!ME',
 '(Historic)Distribution and transmission!$!100!$!PD',
 '(Historic)Agricultural credit!$!100!$!AC',
 '(Historic)Telecommunications and informatics!$!100!$!CC',
 'Other Agriculture; Fishing and Forestry!$!100!$!AZ',
 '(Historic)Thermal!$!100!$!PT',
 'Other Energy and Extractives!$!100!$!LZ',
 'Power!$!100!$!LD',
 'Livestock!$!100!$!AL',
 'Central Government (Central Agencies)!$!50!$!BC',
 'Primary Education!$!100!$!EP',
 '(Historic)Perennial crops!$!100!$!AP',
 '(Historic)Other industry!$!100!$!IY',
 'Secondary Education!$!100!$!ES',
 'Vocational training!$!100!$!EV',
 '(Historic)Public sector management adjustment!$!100!$!BB',
 '(Historic)Other finance!$!100!$!FY',
 'Tertiary Education!$!100!$!ET',
 'Oil and Gas!$!100!$!LC',
 'Other Industry; Trade and Services!$!100!$!YZ',
 '(Historic)Oil and gas exploration and development!$!100!$!GI',
 '(Historic)Electric power and other energy adjustment!$!100!$!PP',
 'Sub-National Government!$!100!$!BH',
 'Central Government (Central Agencies)!$!40!$!BC',
 'Renewable energy!$!100!$!LE',
 '(Historic)Urban housing!$!100!$!UH',
 'Energy Transmission and Distribution!$!100!$!LT',
 'Renewable Energy Biomass!$!25!$!LB',
 'Law and Justice!$!100!$!BG',
 '(Historic)Urban management!$!100!$!UM',
 'Micro- and SME finance!$!100!$!FE',
 '(Historic)Fertilizer and other chemicals!$!100!$!IC',
 'Rural and Inter-Urban Roads!$!100!$!TI',
 'Central Government (Central Agencies)!$!60!$!BC',
 'Roads and highways!$!100!$!TA',
 '(Historic)Agro-industry and marketing!$!100!$!AM',
 '(Historic)Industrial adjustment!$!100!$!II',
 'Other Agriculture; Fishing and Forestry!$!50!$!AZ',
 '(Historic)Mining and other extractive!$!100!$!NN',
 '(Historic)Other power and energy conversion!$!100!$!PY',
 '(Historic)Small scale enterprise!$!100!$!IL',
 'Water Supply!$!100!$!WC',
 '(Historic)Other agriculture!$!100!$!AY',
 'Other Non-bank Financial Institutions!$!100!$!FL',
 '(Historic)Agricultural extension!$!100!$!AE',
 'Social Protection!$!50!$!SA',
 'Waste Management!$!100!$!WB',
 'Other Public Administration!$!50!$!BZ',
 'Health!$!50!$!HG',
 'Other Water Supply; Sanitation and Waste Management!$!100!$!WZ',
 'Other Education!$!100!$!EZ',
 '(Historic)Other urban development!$!100!$!UY',
 'Banking Institutions!$!75!$!FA',
 'Banking Institutions!$!50!$!FA',
 'Other Agriculture; Fishing and Forestry!$!40!$!AZ',
 'Central Government (Central Agencies)!$!45!$!BC',
 'Social Protection!$!40!$!SA',
 'Other Public Administration!$!40!$!BZ',
 'Sub-National Government!$!50!$!BH',
 'Fisheries!$!100!$!AF',
 'Other Industry; Trade and Services!$!50!$!YZ',
 'Health!$!90!$!HG',
 'Central Government (Central Agencies)!$!80!$!BC',
 '(Historic)Other transportation!$!100!$!TY',
 '(Historic)Trade policy reform!$!100!$!MT',
 'Agricultural Extension; Research; and Other Support Activities!$!100!$!AB',
 'Central Government (Central Agencies)!$!44!$!BC',
 'Other Water Supply; Sanitation and Waste Management!$!70!$!WZ',
 '(Historic)Targeted health!$!100!$!HT',
 'Other Agriculture; Fishing and Forestry!$!60!$!AZ',
 'Mining!$!100!$!LM',
 'Central Government (Central Agencies)!$!70!$!BC',
 'Sewerage!$!100!$!WS',
 'Banking Institutions!$!100!$!FA',
 '(Historic)Basic health!$!100!$!HB',
 '(Historic)Research!$!100!$!AR',
 '(Historic)Education adjustment!$!100!$!EE',
 'Public Administration - Social Protection!$!100!$!SG',
 'Other Agriculture; Fishing and Forestry!$!80!$!AZ',
 'Social Protection!$!60!$!SA',
 'Other Information and Communications Technologies!$!100!$!CZ',
 'ICT Infrastructure!$!100!$!CI',
 'Insurance and Pension!$!100!$!FD',
 'Rural and Inter-Urban Roads!$!90!$!TI',
 'Central Government (Central Agencies)!$!75!$!BC',
 'Central Government (Central Agencies)!$!90!$!BC',
 'Renewable Energy Biomass!$!100!$!LB',
 'Sub-National Government!$!40!$!BH',
 'Rural and Inter-Urban Roads!$!95!$!TI',
 'Other Public Administration!$!60!$!BZ',
 'Other Agriculture; Fishing and Forestry!$!70!$!AZ',
 'Health!$!70!$!HG',
 '(Historic)Other education!$!100!$!EY',
 'Central Government (Central Agencies)!$!65!$!BC',
 'Health!$!80!$!HG',
 'Central Government (Central Agencies)!$!67!$!BC',
 'Other Water Supply; Sanitation and Waste Management!$!50!$!WZ',
 '(Historic)Refining; storage and distribution!$!100!$!GS',
 'Central Government (Central Agencies)!$!57!$!BC',
 'Central Government (Central Agencies)!$!30!$!BC',
 'Health!$!91!$!HG',
 'Forestry!$!50!$!AT',
 'Sanitation!$!100!$!WA',
 'Central Government (Central Agencies)!$!38!$!BC',
 'Social Protection!$!80!$!SA',
 'Other Water Supply; Sanitation and Waste Management!$!40!$!WZ',
 'Central Government (Central Agencies)!$!46!$!BC',
 'Central Government (Central Agencies)!$!32!$!BC',
 'Central Government (Central Agencies)!$!56!$!BC',
 'Central Government (Central Agencies)!$!58!$!BC',
 'Primary Education!$!40!$!EP',
 'Workforce Development and Vocational Education!$!100!$!EW',
 'Urban Transport!$!100!$!TC',
 'Rural and Inter-Urban Roads!$!97!$!TI',
 'Rural and Inter-Urban Roads!$!99!$!TI',
 'Social Protection!$!30!$!SA',
 '(Historic)Urban transport!$!100!$!TU',
 'Water Supply!$!60!$!WC',
 'Rural and Inter-Urban Roads!$!96!$!TI',
 'Water Supply!$!50!$!WC',
 'Central Government (Central Agencies)!$!41!$!BC',
 'Central Government (Central Agencies)!$!43!$!BC',
 'Central Government (Central Agencies)!$!85!$!BC',
 'Renewable Energy Hydro!$!100!$!LH',
 'Roads and highways!$!99!$!TA',
 'Central Government (Central Agencies)!$!78!$!BC',
 'Forestry!$!60!$!AT',
 'Health!$!40!$!HG',
 'Central Government (Central Agencies)!$!55!$!BC',
 'Other Industry; Trade and Services!$!40!$!YZ',
 'Primary Education!$!50!$!EP',
 '(Historic)Non-sector specific!$!100!$!MY',
 'Rural and Inter-Urban Roads!$!94!$!TI',
 'Other Industry; Trade and Services!$!46!$!YZ',
 'Other Water Supply; Sanitation and Waste Management!$!60!$!WZ',
 'Health!$!100!$!JA',
 'Central Government (Central Agencies)!$!36!$!BC',
 'Social Protection!$!90!$!SA',
 'Water Supply!$!70!$!WC',
 'Public Administration - Financial Sector!$!100!$!FP',
 'Social Protection!$!75!$!SA',
 'Social Protection!$!70!$!SA',
 '(Historic)Oil and gas transportation!$!100!$!GP',
 'Health!$!75!$!HG',
 'Central Government (Central Agencies)!$!63!$!BC',
 'Central Government (Central Agencies)!$!68!$!BC',
 'Health!$!97!$!HG',
 'Central Government (Central Agencies)!$!35!$!BC',
 'Other Industry; Trade and Services!$!45!$!YZ',
 'Central Government (Central Agencies)!$!34!$!BC',
 'Agricultural Extension; Research; and Other Support Activities!$!50!$!AB',
 'Health!$!94!$!HG',
 'Renewable Energy Solar!$!100!$!LU',
 'Health!$!60!$!HG',
 '(Historic)Financial adjustment!$!100!$!FF',
 'Agricultural Extension; Research; and Other Support Activities!$!20!$!AB',
 'Central Government (Central Agencies)!$!33!$!BC',
 'Social Protection!$!35!$!SA',
 'Rural and Inter-Urban Roads!$!80!$!TI',
 'Health!$!95!$!HG',
 'Primary Education!$!70!$!EP',
 'Irrigation and Drainage!$!50!$!AI',
 '(Historic)Urban development adjustment!$!100!$!UU',
 'Roads and highways!$!98!$!TA',
 'Other Public Administration!$!30!$!BZ',
 'Health!$!88!$!HG',
 'Roads and highways!$!97!$!TA',
 'Water Supply!$!80!$!WC',
 'Primary Education!$!60!$!EP',
 'Other Energy and Extractives!$!50!$!LZ',
 'Roads and highways!$!91!$!TA',
 'Banking Institutions!$!60!$!FA',
 'Public Administration - Water; Sanitation and Waste Management!$!100!$!WF',
 '(Historic)Industrial restructuring!$!100!$!IR',
 'Central Government (Central Agencies)!$!88!$!BC',
 'Water Supply!$!75!$!WC',
 'Sub-National Government!$!60!$!BH',
 'Sub-National Government!$!30!$!BH',
 'Central Government (Central Agencies)!$!42!$!BC',
 'Health!$!96!$!HG',
 'Social Protection!$!89!$!SA',
 '(Historic)Transportation adjustment!$!100!$!TT',
 '(Historic)Annual crops!$!100!$!AQ',
 '(Historic)Water supply and sanitation adjustment!$!100!$!WW',
 'ICT Services!$!50!$!CS',
 'Banking Institutions!$!37!$!FA',
 'Agricultural Extension; Research; and Other Support Activities!$!80!$!AB',
 'Central Government (Central Agencies)!$!72!$!BC',
 'Other Non-bank Financial Institutions!$!50!$!FL',
 'Power!$!50!$!LD',
 'Power!$!99!$!LD',
 '(Historic)Rural roads!$!100!$!TR',
 'Other Education!$!50!$!EZ',
 'Other Transportation!$!100!$!TZ',
 'Other Water Supply; Sanitation and Waste Management!$!48!$!WZ',
 'Forestry!$!80!$!AT',
 'Irrigation and Drainage!$!60!$!AI',
 'Other Public Administration!$!35!$!BZ',
 'Primary Education!$!80!$!EP',
 'Tertiary Education!$!50!$!ET',
 'Other Industry; Trade and Services!$!96!$!YZ',
 'Social Protection!$!93!$!SA',
 'Other Agriculture; Fishing and Forestry!$!65!$!AZ',
 'Tertiary Education!$!90!$!ET',
 'Central Government (Central Agencies)!$!95!$!BC',
 'Irrigation and Drainage!$!90!$!AI',
 'Agricultural Extension; Research; and Other Support Activities!$!60!$!AB',
 'Central Government (Central Agencies)!$!84!$!BC',
 'Social Protection!$!27!$!SA',
 'Health!$!93!$!HG',
 'Central Government (Central Agencies)!$!29!$!BC',
 'Other Water Supply; Sanitation and Waste Management!$!30!$!WZ',
 'Roads and highways!$!90!$!TA',
 'Oil and Gas!$!50!$!LC',
 'Sanitation!$!50!$!WA',
 '(Historic)Other water supply and sanitation!$!100!$!WY',
 'Other Industry; Trade and Services!$!67!$!YZ',
 'Central Government (Central Agencies)!$!39!$!BC',
 'Other Agriculture; Fishing and Forestry!$!35!$!AZ',
 'Central Government (Central Agencies)!$!66!$!BC',
 'Other Industry; Trade and Services!$!30!$!YZ',
 'Workforce Development and Vocational Education!$!50!$!EW',
 'Primary Education!$!75!$!EP',
 'Public Administration - Energy and Extractives!$!100!$!LP',
 'Renewable energy!$!50!$!LE',
 '(Historic)Macro/non-trade!$!100!$!KN',
 'Other Public Administration!$!45!$!BZ',
 'Health!$!87!$!HG',
 'Other Agriculture; Fishing and Forestry!$!49!$!AZ',
 'Rural and Inter-Urban Roads!$!92!$!TI',
 'Health!$!65!$!HG',
 'Central Government (Central Agencies)!$!37!$!BC',
 'Other Non-bank Financial Institutions!$!30!$!FL',
 'Central Government (Central Agencies)!$!64!$!BC',
 'Central Government (Central Agencies)!$!25!$!BC',
 'Other Industry; Trade and Services!$!98!$!YZ',
 'Fisheries!$!50!$!AF',
 'Power!$!95!$!LD',
 'Other Industry; Trade and Services!$!38!$!YZ',
 'Other Agriculture; Fishing and Forestry!$!25!$!AZ',
 'Primary Education!$!56!$!EP',
 'Other Water Supply; Sanitation and Waste Management!$!45!$!WZ',
 'Central Government (Central Agencies)!$!61!$!BC',
 'Rural and Inter-Urban Roads!$!40!$!TI',
 'Other Industry; Trade and Services!$!55!$!YZ',
 'Water Supply!$!65!$!WC',
 'Agricultural Extension; Research; and Other Support Activities!$!40!$!AB',
 'Central Government (Central Agencies)!$!62!$!BC',
 'Other Industry; Trade and Services!$!41!$!YZ',
 'Secondary Education!$!50!$!ES',
 'Public Administration - Agriculture; Fishing & Forestry!$!50!$!AK',
 'Irrigation and Drainage!$!80!$!AI',
 'Other Industry; Trade and Services!$!44!$!YZ',
 'Other Agriculture; Fishing and Forestry!$!20!$!AZ',
 'Sub-National Government!$!70!$!BH',
 'Health!$!30!$!HG',
 'Health!$!74!$!HG',
 'Central Government (Central Agencies)!$!53!$!BC',
 'Irrigation and Drainage!$!75!$!AI',
 'Water Supply!$!40!$!WC',
 'Public Administration - Social Protection!$!50!$!SG',
 'Health!$!83!$!HG',
 'Other Water Supply; Sanitation and Waste Management!$!35!$!WZ',
 'Central Government (Central Agencies)!$!76!$!BC',
 'Central Government (Central Agencies)!$!31!$!BC',
 'Other Industry; Trade and Services!$!60!$!YZ',
 'Social Protection!$!96!$!SA',
 'Public Administration - Industry; Trade and Services!$!100!$!YF',
 'Crops!$!100!$!AH',
 'Other Energy and Extractives!$!70!$!LZ',
 'Health!$!86!$!HG',
 'Central Government (Central Agencies)!$!92!$!BC',
 'Other Industry; Trade and Services!$!35!$!YZ',
 'Other Agriculture; Fishing and Forestry!$!34!$!AZ',
 'Central Government (Central Agencies)!$!47!$!BC',
 'Roads and highways!$!93!$!TA',
 'Water Supply!$!95!$!WC',
 'Crops!$!50!$!AH',
 'Other Water Supply; Sanitation and Waste Management!$!25!$!WZ',
 'Social Protection!$!49!$!SA',
 'Central Government (Central Agencies)!$!52!$!BC',
 'Central Government (Central Agencies)!$!82!$!BC',
 'Mining!$!50!$!LM',
 'Early Childhood Education!$!100!$!EC',
 'Irrigation and Drainage!$!70!$!AI',
 'Water Supply!$!30!$!WC',
 'Rural and Inter-Urban Roads!$!20!$!TI',
 'Central Government (Central Agencies)!$!48!$!BC',
 'Other Transportation!$!40!$!TZ',
 'Social Protection!$!55!$!SA',
 'Social Protection!$!45!$!SA',
 'Power!$!98!$!LD',
 'Sub-National Government!$!35!$!BH',
 'Central Government (Central Agencies)!$!73!$!BC',
 'Health!$!98!$!HG',
 'Rural and Inter-Urban Roads!$!50!$!TI',
 'Other Public Administration!$!25!$!BZ',
 'Social Protection!$!46!$!SA',
 'Other Non-bank Financial Institutions!$!40!$!FL',
 'Health!$!84!$!HG',
 'Rural and Inter-Urban Roads!$!98!$!TI',
 'Primary Education!$!65!$!EP',
 'Other Public Administration!$!34!$!BZ',
 'Social Protection!$!34!$!SA',
 'Central Government (Central Agencies)!$!98!$!BC',
 'Other Agriculture; Fishing and Forestry!$!43!$!AZ',
 'Other Agriculture; Fishing and Forestry!$!45!$!AZ',
 'Secondary Education!$!70!$!ES',
 'Rural and Inter-Urban Roads!$!87!$!TI',
 'Other Energy and Extractives!$!48!$!LZ',
 'Central Government (Central Agencies)!$!54!$!BC',
 'Health!$!92!$!HG',
 'Water Supply!$!90!$!WC',
 'Rural and Inter-Urban Roads!$!83!$!TI',
 'Banking Institutions!$!55!$!FA',
 'Other Agriculture; Fishing and Forestry!$!30!$!AZ',
 'Other Agriculture; Fishing and Forestry!$!55!$!AZ',
 'Health!$!85!$!HG',
 'Social Protection!$!61!$!SA',
 'Central Government (Central Agencies)!$!49!$!BC',
 '(Historic)Pollution control / waste management!$!100!$!VP',
 'Public Administration - Information and Communications Technologies!$!100!$!CF',
 'Primary Education!$!55!$!EP',
 'Other Industry; Trade and Services!$!29!$!YZ',
 'Social Protection!$!88!$!SA',
 'Primary Education!$!90!$!EP',
 'Agricultural markets; commercialization and agri-business!$!40!$!YA',
 'Micro- and SME finance!$!50!$!FE',
 'Other Industry; Trade and Services!$!70!$!YZ',
 'Forestry!$!70!$!AT',
 'Non-Renewable Energy Generation!$!100!$!LN',
 'Secondary Education!$!60!$!ES',
 'Power!$!90!$!LD',
 'Central Government (Central Agencies)!$!94!$!BC',
 'Rural and Inter-Urban Roads!$!70!$!TI',
 'Tertiary Education!$!95!$!ET',
 'Other Industry; Trade and Services!$!39!$!YZ',
 'Social Protection!$!85!$!SA',
 'Banking Institutions!$!70!$!FA',
 'Irrigation and Drainage!$!65!$!AI',
 'Roads and highways!$!96!$!TA',
 'Health!$!73!$!HG',
 'Social Protection!$!82!$!SA',
 'Social Protection!$!95!$!SA',
 'Health!$!82!$!HG',
 'Central Government (Central Agencies)!$!96!$!BC',
 'Rural and Inter-Urban Roads!$!93!$!TI',
 'Water Supply!$!87!$!WC',
 'Other Industry; Trade and Services!$!84!$!YZ',
 'Crops!$!80!$!AH',
 'Other Energy and Extractives!$!40!$!LZ',
 'Other Industry; Trade and Services!$!43!$!YZ',
 'Primary Education!$!35!$!EP',
 'Primary Education!$!45!$!EP',
 'Other Water Supply; Sanitation and Waste Management!$!67!$!WZ',
 'Social Protection!$!33!$!SA',
 'Rural and Inter-Urban Roads!$!60!$!TI',
 '(Historic)Rural water supply and sanitation!$!100!$!WR',
 'Urban Transport!$!97!$!TC',
 'Other Industry; Trade and Services!$!49!$!YZ',
 'Social Protection!$!84!$!SA',
 'Rural and Inter-Urban Roads!$!91!$!TI',
 'Social Protection!$!94!$!SA',
 'Other Industry; Trade and Services!$!57!$!YZ',
 'Health!$!57!$!HG',
 'Crops!$!60!$!AH',
 'Other Industry; Trade and Services!$!90!$!YZ',
 'Other Education!$!30!$!EZ',
 'Energy Transmission and Distribution!$!86!$!LT',
 'Other Agriculture; Fishing and Forestry!$!39!$!AZ',
 'Other Energy and Extractives!$!98!$!LZ',
 'Sub-National Government!$!65!$!BH',
 'Capital Markets!$!100!$!FK',
 'Waste Management!$!80!$!WB',
 'Central Government (Central Agencies)!$!83!$!BC',
 'Central Government (Central Agencies)!$!87!$!BC',
 'Sub-National Government!$!32!$!BH',
 'Primary Education!$!83!$!EP',
 'Other Education!$!40!$!EZ',
 'Other Non-bank Financial Institutions!$!10!$!FL',
 'Social Protection!$!68!$!SA',
 'Health!$!68!$!HG',
 'Irrigation and Drainage!$!40!$!AI',
 'Primary Education!$!30!$!EP',
 'Renewable Energy Biomass!$!50!$!LB',
 'Other Agriculture; Fishing and Forestry!$!42!$!AZ',
 'Forestry!$!40!$!AT',
 'Health!$!89!$!HG',
 'Agricultural markets; commercialization and agri-business!$!50!$!YA',
 'Other Industry; Trade and Services!$!80!$!YZ',
 'Health!$!77!$!HG',
 'Other Industry; Trade and Services!$!97!$!YZ',
 'Irrigation and Drainage!$!26!$!AI',
 'Primary Education!$!25!$!EP',
 'Social Protection!$!81!$!SA',
 'Other Water Supply; Sanitation and Waste Management!$!56!$!WZ',
 'Urban Transport!$!96!$!TC',
 'Central Government (Central Agencies)!$!71!$!BC',
 'Primary Education!$!64!$!EP',
 'Agro-industry!$!100!$!YB',
 'Social Protection!$!44!$!SA',
 'Other Energy and Extractives!$!80!$!LZ',
 'Irrigation and Drainage!$!96!$!AI',
 'Social Protection!$!20!$!SA',
 'Sub-National Government!$!27!$!BH',
 'Social Protection!$!25!$!SA',
 'Health!$!55!$!HG',
 'Secondary Education!$!40!$!ES',
 'Roads and highways!$!95!$!TA',
 'Roads and highways!$!89!$!TA',
 'Agricultural Extension; Research; and Other Support Activities!$!30!$!AB',
 'Other Agriculture; Fishing and Forestry!$!33!$!AZ',
 'Other Public Administration!$!70!$!BZ',
 'Forestry!$!87!$!AT',
 'Health!$!79!$!HG',
 'Social Protection!$!57!$!SA',
 'Renewable energy!$!70!$!LE',
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 'Sub-National Government!$!59!$!BH',
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 'Irrigation and Drainage!$!97!$!AI',
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 'Social Protection!$!86!$!SA',
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 'Social Protection!$!99!$!SA',
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 'Public Administration - Agriculture; Fishing & Forestry!$!100!$!AK',
 'Public Administration - Transportation!$!100!$!TF',
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 'Law and Justice!$!50!$!BG',
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 'Primary Education!$!46!$!EP',
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 'Other Water Supply; Sanitation and Waste Management!$!32!$!WZ',
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 'Railways!$!99!$!TW',
 'Social Protection!$!87!$!SA',
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 'Primary Education!$!38!$!EP',
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 'Social Protection!$!39!$!SA',
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 'Energy Transmission and Distribution!$!75!$!LT',
 '(Historic)Other population; health and nutrition!$!100!$!HY',
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 'Roads and highways!$!51!$!TA',
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 'Rural and Inter-Urban Roads!$!89!$!TI',
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 'Housing Construction!$!100!$!YH',
 'Water Supply!$!81!$!WC',
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 'Social Protection!$!62!$!SA',
 'Banking Institutions!$!36!$!FA',
 'Health!$!54!$!HG',
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 'Water Supply!$!89!$!WC',
 'Health!$!69!$!HG',
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 'Tertiary Education!$!70!$!ET',
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 'Water Supply!$!96!$!WC',
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 'Trade!$!40!$!YY',
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 'Roads and highways!$!80!$!TA',
 'Social Protection!$!69!$!SA',
 'Sanitation!$!80!$!WA',
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 'Forestry!$!65!$!AT',
 'Power!$!97!$!LD',
 'Roads and highways!$!85!$!TA',
 '(Historic)Other environment!$!100!$!VY',
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 'Banking Institutions!$!40!$!FA',
 'Health!$!35!$!HG',
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 'Waste Management!$!50!$!WB',
 'Sanitation!$!49!$!WA',
 'Roads and highways!$!92!$!TA',
 'Rural and Inter-Urban Roads!$!82!$!TI',
 'Tertiary Education!$!99!$!ET',
 'Social Protection!$!78!$!SA',
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 'Crops!$!35!$!AH',
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 'Social Protection!$!43!$!SA',
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 'Other Water Supply; Sanitation and Waste Management!$!51!$!WZ',
 'Health!$!46!$!HG',
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 'Health!$!78!$!HG',
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 'Water Supply!$!45!$!WC',
 'Irrigation and Drainage!$!48!$!AI',
 'Rural and Inter-Urban Roads!$!67!$!TI',
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 'Banking Institutions!$!80!$!FA',
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 'Renewable Energy Biomass!$!20!$!LB',
 'Roads and highways!$!40!$!TA',
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 'Primary Education!$!66!$!EP',
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 'Fisheries!$!40!$!AF',
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 'Health!$!25!$!HG',
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 'Central Government (Central Agencies)!$!27!$!BC',
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 'Social Protection!$!73!$!SA',
 'Social Protection!$!66!$!SA',
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 'Other Agriculture; Fishing and Forestry!$!59!$!AZ',
 'Sanitation!$!45!$!WA',
 'Early Childhood Education!$!50!$!EC',
 'Water Supply!$!85!$!WC',
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 'Other Industry; Trade and Services!$!93!$!YZ',
 'Social Protection!$!37!$!SA',
 'Fisheries!$!34!$!AF',
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 'Water Supply!$!33!$!WC',
 'Irrigation and Drainage!$!62!$!AI',
 'Agricultural markets; commercialization and agri-business!$!48!$!YA',
 'Forestry!$!75!$!AT',
 'Crops!$!40!$!AH',
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 'Primary Education!$!71!$!EP',
 'Other Water Supply; Sanitation and Waste Management!$!49!$!WZ',
 'Urban Transport!$!49!$!TC',
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 'Other Water Supply; Sanitation and Waste Management!$!58!$!WZ',
 'Other Water Supply; Sanitation and Waste Management!$!76!$!WZ',
 'Public Administration - Social Protection!$!70!$!SG',
 'Waste Management!$!90!$!WB',
 'Power!$!93!$!LD',
 'Other industry!$!100!$!YW',
 'Central Government (Central Agencies)!$!91!$!BC',
 'Rural and Inter-Urban Roads!$!86!$!TI',
 'Irrigation and Drainage!$!20!$!AI',
 'Fisheries!$!35!$!AF',
 'Energy Transmission and Distribution!$!91!$!LT',
 'Primary Education!$!59!$!EP',
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 'Agricultural markets; commercialization and agri-business!$!43!$!YA',
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 'Sanitation!$!35!$!WA',
 'Other Education!$!34!$!EZ',
 'Primary Education!$!76!$!EP',
 'Banking Institutions!$!76!$!FA',
 'Other Industry; Trade and Services!$!88!$!YZ',
 'Banking Institutions!$!43!$!FA',
 'Other Education!$!33!$!EZ',
 'Other Agriculture; Fishing and Forestry!$!47!$!AZ',
 'Sanitation!$!94!$!WA',
 'Other Agriculture; Fishing and Forestry!$!36!$!AZ',
 'Public Administration - Energy and Extractives!$!9!$!LP',
 'Central Government (Central Agencies)!$!89!$!BC',
 'Agricultural Extension; Research; and Other Support Activities!$!70!$!AB',
 'Forestry!$!42!$!AT',
 'Urban Transport!$!99!$!TC',
 'Micro- and SME finance!$!96!$!FE',
 'Central Government (Central Agencies)!$!10!$!BC',
 'Public Administration - Agriculture; Fishing & Forestry!$!9!$!AK',
 'Health!$!38!$!HG',
 'Other Transportation!$!70!$!TZ',
 'Public Administration - Agriculture; Fishing & Forestry!$!5!$!AK',
 'Public Administration - Health!$!100!$!HF',
 'Other Industry; Trade and Services!$!48!$!YZ',
 'Sub-National Government!$!78!$!BH',
 'Irrigation and Drainage!$!79!$!AI',
 'Social Protection!$!97!$!SA',
 'Banking Institutions!$!87!$!FA',
 'Crops!$!70!$!AH',
 'Forestry!$!84!$!AT',
 'Other Public Administration!$!56!$!BZ',
 'Oil and Gas!$!34!$!LC',
 'Urban Transport!$!37!$!TC',
 'Roads and highways!$!81!$!TA',
 'Other Energy and Extractives!$!55!$!LZ',
 'Primary Education!$!68!$!EP',
 'Other Energy and Extractives!$!91!$!LZ',
 'Other Water Supply; Sanitation and Waste Management!$!85!$!WZ',
 'Other Transportation!$!50!$!TZ',
 'Energy Transmission and Distribution!$!93!$!LT',
 'Other Public Administration!$!54!$!BZ',
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 'Other Water Supply; Sanitation and Waste Management!$!59!$!WZ',
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 'Primary Education!$!34!$!EP',
 'Banking Institutions!$!94!$!FA',
 'Ports/Waterways!$!97!$!TP',
 'Rural and Inter-Urban Roads!$!64!$!TI',
 'Other Water Supply; Sanitation and Waste Management!$!33!$!WZ',
 'Renewable Energy Biomass!$!23!$!LB',
 'Renewable Energy Hydro!$!93!$!LH',
 'Agro-industry!$!37!$!YB',
 'Water Supply!$!66!$!WC',
 'Irrigation and Drainage!$!71!$!AI',
 'Railways!$!90!$!TW',
 'Water Supply!$!71!$!WC',
 'Health!$!34!$!HG',
 'Sub-National Government!$!67!$!BH',
 'Energy Transmission and Distribution!$!67!$!LT',
 'Banking Institutions!$!73!$!FA',
 'Energy Transmission and Distribution!$!64!$!LT',
 'Adult; Basic and Continuing Education!$!100!$!EL',
 'Primary Education!$!41!$!EP',
 'Urban Transport!$!50!$!TC',
 'Power!$!94!$!LD',
 'Other Education!$!35!$!EZ',
 'Other Public Administration!$!49!$!BZ',
 'Social Protection!$!98!$!SA',
 'Primary Education!$!78!$!EP',
 'Roads and highways!$!55!$!TA',
 'Other Water Supply; Sanitation and Waste Management!$!68!$!WZ',
 'Other Water Supply; Sanitation and Waste Management!$!66!$!WZ',
 'Roads and highways!$!67!$!TA',
 'Other Non-bank Financial Institutions!$!15!$!FL',
 'Other Energy and Extractives!$!49!$!LZ',
 'Agricultural markets; commercialization and agri-business!$!46!$!YA',
 'Roads and highways!$!45!$!TA',
 'Social Protection!$!29!$!SA',
 'Other Energy and Extractives!$!35!$!LZ',
 'Urban Transport!$!95!$!TC',
 'Water Supply!$!69!$!WC',
 'Social Protection!$!47!$!SA',
 'Other Agriculture; Fishing and Forestry!$!41!$!AZ',
 'Sub-National Government!$!38!$!BH',
 'Water Supply!$!52!$!WC',
 'ICT Infrastructure!$!30!$!CI',
 'Health!$!64!$!HG',
 'Sub-National Government!$!80!$!BH',
 'Secondary Education!$!33!$!ES',
 'Social Protection!$!67!$!SA',
 'Roads and highways!$!47!$!TA',
 'Public Administration - Agriculture; Fishing & Forestry!$!54!$!AK',
 'Irrigation and Drainage!$!94!$!AI',
 'Sub-National Government!$!46!$!BH',
 'Aviation!$!100!$!TV',
 'Social Protection!$!83!$!SA',
 'Primary Education!$!44!$!EP',
 'Rural and Inter-Urban Roads!$!73!$!TI',
 'Irrigation and Drainage!$!55!$!AI',
 'Other Industry; Trade and Services!$!58!$!YZ',
 'Other Water Supply; Sanitation and Waste Management!$!81!$!WZ',
 'Energy Transmission and Distribution!$!90!$!LT',
 'Other Public Administration!$!65!$!BZ',
 'Insurance and Pension!$!70!$!FD',
 'Energy Transmission and Distribution!$!81!$!LT',
 'Ports/Waterways!$!60!$!TP',
 'Other Water Supply; Sanitation and Waste Management!$!22!$!WZ',
 'Roads and highways!$!43!$!TA',
 'Water Supply!$!46!$!WC',
 'Health!$!20!$!HG',
 'Roads and highways!$!64!$!TA',
 'Banking Institutions!$!10!$!FA',
 'Public Administration - Water; Sanitation and Waste Management!$!40!$!WF',
 'Health!$!59!$!HG',
 'Energy Transmission and Distribution!$!50!$!LT',
 'Social Protection!$!79!$!SA',
 'Banking Institutions!$!41!$!FA',
 'Health!$!61!$!HG',
 'Crops!$!51!$!AH',
 'Health!$!62!$!HG',
 'Renewable Energy Biomass!$!40!$!LB',
 'Agricultural markets; commercialization and agri-business!$!29!$!YA',
 'Irrigation and Drainage!$!67!$!AI',
 'Health!$!81!$!HG',
 'Rural and Inter-Urban Roads!$!81!$!TI',
 'Power!$!70!$!LD',
 'Sub-National Government!$!90!$!BH',
 'Other Non-bank Financial Institutions!$!60!$!FL',
 'Other Industry; Trade and Services!$!94!$!YZ',
 'Primary Education!$!93!$!EP',
 'Primary Education!$!95!$!EP',
 'Water Supply!$!35!$!WC',
 'Health!$!71!$!HG',
 'Public Administration - Financial Sector!$!50!$!FP',
 'Primary Education!$!96!$!EP',
 'Primary Education!$!69!$!EP',
 'Central Government (Central Agencies)!$!86!$!BC',
 'Water Supply!$!56!$!WC',
 'Central Government (Central Agencies)!$!97!$!BC',
 'Water Supply!$!63!$!WC',
 'Rural and Inter-Urban Roads!$!59!$!TI',
 'Rural and Inter-Urban Roads!$!32!$!TI',
 'Banking Institutions!$!98!$!FA',
 'Health!$!76!$!HG',
 'Water Supply!$!53!$!WC',
 'Social Protection!$!72!$!SA',
 'Agricultural markets; commercialization and agri-business!$!39!$!YA',
 'Workforce Development and Vocational Education!$!60!$!EW',
 'Other Non-bank Financial Institutions!$!43!$!FL',
 'Energy Transmission and Distribution!$!80!$!LT',
 'Mining!$!60!$!LM',
 'Workforce Development and Vocational Education!$!80!$!EW',
 'Rural and Inter-Urban Roads!$!22!$!TI',
 'Secondary Education!$!35!$!ES',
 'Health!$!49!$!HG',
 'Primary Education!$!42!$!EP',
 'Central Government (Central Agencies)!$!99!$!BC',
 'Housing Construction!$!50!$!YH',
 'Water Supply!$!62!$!WC',
 'Other Agriculture; Fishing and Forestry!$!85!$!AZ',
 'Central Government (Central Agencies)!$!8!$!BC',
 '(Historic)Other public sector management!$!100!$!BY',
 'Water Supply!$!94!$!WC',
 'Power!$!80!$!LD',
 'Health!$!58!$!HG',
 'Roads and highways!$!75!$!TA',
 '(Historic)Institutional Development!$!100!$!BI',
 'Banking Institutions!$!33!$!FA',
 'Other Water Supply; Sanitation and Waste Management!$!27!$!WZ',
 'Agricultural Extension; Research; and Other Support Activities!$!49!$!AB',
 'Ports/Waterways!$!99!$!TP',
 'Other Information and Communications Technologies!$!60!$!CZ',
 'Banking Institutions!$!93!$!FA',
 'Water Supply!$!88!$!WC',
 'Sanitation!$!85!$!WA',
 'Agricultural markets; commercialization and agri-business!$!41!$!YA',
 'Banking Institutions!$!78!$!FA',
 'Other Agriculture; Fishing and Forestry!$!44!$!AZ',
 'Roads and highways!$!72!$!TA',
 'Water Supply!$!84!$!WC',
 'Other Public Administration!$!41!$!BZ',
 'Other Education!$!49!$!EZ',
 'Other Public Administration!$!29!$!BZ',
 'Urban Transport!$!57!$!TC',
 'Rural and Inter-Urban Roads!$!39!$!TI',
 'Other Water Supply; Sanitation and Waste Management!$!26!$!WZ',
 'Health!$!56!$!HG',
 'Micro- and SME finance!$!90!$!FE',
 'Other Non-bank Financial Institutions!$!35!$!FL',
 'Primary Education!$!54!$!EP',
 'Urban Transport!$!55!$!TC',
 'Social Protection!$!28!$!SA',
 'Oil and Gas!$!80!$!LC',
 'Workforce Development and Vocational Education!$!68!$!EW',
 'Power!$!91!$!LD',
 'Other Public Administration!$!38!$!BZ',
 'Agricultural Extension; Research; and Other Support Activities!$!53!$!AB',
 'Water Supply!$!92!$!WC',
 'Waste Management!$!40!$!WB',
 'Irrigation and Drainage!$!38!$!AI',
 'Water Supply!$!73!$!WC',
 'Central Government (Central Agencies)!$!23!$!BC',
 'Sanitation!$!96!$!WA',
 'Renewable Energy Hydro!$!85!$!LH',
 'Agricultural Extension; Research; and Other Support Activities!$!8!$!AB',
 'Other Transportation!$!28!$!TZ',
 'Banking Institutions!$!51!$!FA',
 'Water Supply!$!42!$!WC',
 'Energy Transmission and Distribution!$!97!$!LT',
 'Agricultural Extension; Research; and Other Support Activities!$!75!$!AB',
 'Water Supply!$!97!$!WC',
 'Social Protection!$!21!$!SA',
 'Water Supply!$!47!$!WC',
 'Irrigation and Drainage!$!83!$!AI',
 'Workforce Development and Vocational Education!$!87!$!EW',
 'Sub-National Government!$!73!$!BH',
 'Other Water Supply; Sanitation and Waste Management!$!53!$!WZ',
 'Other Transportation!$!23!$!TZ',
 'Water Supply!$!51!$!WC',
 'Other Agriculture; Fishing and Forestry!$!62!$!AZ',
 'Renewable Energy Wind!$!100!$!LW',
 'Crops!$!64!$!AH',
 'Other Industry; Trade and Services!$!37!$!YZ',
 'Water Supply!$!74!$!WC',
 'Sanitation!$!92!$!WA',
 'Irrigation and Drainage!$!85!$!AI',
 'Sub-National Government!$!10!$!BH',
 'Agricultural Extension; Research; and Other Support Activities!$!28!$!AB',
 'Other Water Supply; Sanitation and Waste Management!$!61!$!WZ',
 'Fisheries!$!49!$!AF',
 'Tertiary Education!$!94!$!ET',
 'Other Energy and Extractives!$!43!$!LZ',
 'Sub-National Government!$!75!$!BH',
 'Roads and highways!$!71!$!TA',
 'Public Administration - Transportation!$!8!$!TF',
 'Roads and highways!$!25!$!TA',
 'Primary Education!$!74!$!EP',
 'Power!$!36!$!LD',
 'Other Non-bank Financial Institutions!$!12!$!FL',
 'Tertiary Education!$!55!$!ET',
 'Other Industry; Trade and Services!$!51!$!YZ',
 'Social Protection!$!51!$!SA',
 'Primary Education!$!63!$!EP',
 'Irrigation and Drainage!$!69!$!AI',
 'Public Administration - Social Protection!$!60!$!SG',
 'Banking Institutions!$!42!$!FA',
 'Tertiary Education!$!60!$!ET',
 'Tertiary Education!$!35!$!ET',
 'Tertiary Education!$!45!$!ET',
 'Irrigation and Drainage!$!84!$!AI',
 'Workforce Development and Vocational Education!$!78!$!EW',
 'Agricultural Extension; Research; and Other Support Activities!$!71!$!AB',
 'Sewerage!$!48!$!WS',
 'Forestry!$!67!$!AT',
 'Other Education!$!47!$!EZ',
 'Banking Institutions!$!25!$!FA',
 'Workforce Development and Vocational Education!$!90!$!EW',
 'Public Administration - Health!$!5!$!HF',
 'Rural and Inter-Urban Roads!$!45!$!TI',
 'Health!$!48!$!HG',
 'Other Education!$!25!$!EZ',
 'Law and Justice!$!60!$!BG',
 'Other Education!$!36!$!EZ',
 'Tertiary Education!$!62!$!ET',
 'Central Government (Central Agencies)!$!81!$!BC',
 'Central Government (Central Agencies)!$!16!$!BC',
 'Irrigation and Drainage!$!64!$!AI',
 'Other Transportation!$!75!$!TZ',
 'Waste Management!$!70!$!WB',
 'Ports/Waterways!$!80!$!TP',
 'Social Protection!$!36!$!SA',
 'Trade!$!33!$!YY',
 'Central Government (Central Agencies)!$!26!$!BC',
 'Irrigation and Drainage!$!87!$!AI',
 'Other Non-bank Financial Institutions!$!28!$!FL',
 'Primary Education!$!85!$!EP',
 '(Historic)Urban environment!$!100!$!US',
 'Sanitation!$!56!$!WA',
 'Agricultural Extension; Research; and Other Support Activities!$!54!$!AB',
 'Forestry!$!62!$!AT',
 'Primary Education!$!43!$!EP',
 'Other Industry; Trade and Services!$!69!$!YZ',
 'Irrigation and Drainage!$!89!$!AI',
 'Forestry!$!96!$!AT',
 'Energy Transmission and Distribution!$!62!$!LT',
 'Public Administration - Water; Sanitation and Waste Management!$!9!$!WF',
 'Other Agriculture; Fishing and Forestry!$!96!$!AZ',
 'Other Public Administration!$!75!$!BZ',
 'Social Protection!$!76!$!SA',
 'Rural and Inter-Urban Roads!$!38!$!TI',
 'Rural and Inter-Urban Roads!$!72!$!TI',
 'Forestry!$!30!$!AT',
 'Public Administration - Industry; Trade and Services!$!41!$!YF',
 'Waste Management!$!48!$!WB',
 'Secondary Education!$!46!$!ES',
 'Irrigation and Drainage!$!42!$!AI',
 'Urban Transport!$!56!$!TC',
 'Agricultural markets; commercialization and agri-business!$!49!$!YA',
 'Agricultural markets; commercialization and agri-business!$!57!$!YA',
 'Other Industry; Trade and Services!$!54!$!YZ',
 'Roads and highways!$!60!$!TA',
 'Sanitation!$!24!$!WA',
 'Trade!$!100!$!YY',
 'ICT Services!$!40!$!CS',
 'Sub-National Government!$!53!$!BH',
 'Banking Institutions!$!44!$!FA',
 'Irrigation and Drainage!$!54!$!AI',
 'Sub-National Government!$!77!$!BH',
 'Other Agriculture; Fishing and Forestry!$!38!$!AZ',
 'Sub-National Government!$!89!$!BH',
 'Rural and Inter-Urban Roads!$!27!$!TI',
 'Roads and highways!$!54!$!TA',
 'Other Energy and Extractives!$!34!$!LZ',
 'Public Administration - Health!$!6!$!HF',
 'Social Protection!$!74!$!SA',
 'Roads and highways!$!87!$!TA',
 'Energy Transmission and Distribution!$!87!$!LT',
 'Urban Transport!$!80!$!TC',
 'Micro- and SME finance!$!95!$!FE',
 'Tertiary Education!$!89!$!ET',
 'Waste Management!$!95!$!WB',
 'Other Public Administration!$!68!$!BZ',
 ...]

# run this code cell to see the number of unique values
print('Number of unique values in sector1:', len(uniquesectors1))

Number of unique values in sector1: 3060

3060 different categories is quite a lot! Remember that with dummy variables, if you have n categorical values, you need n - 1 new variables! That means 3059 extra columns!

Exercise 2¶

There are a few issues with this 'sector1' variable. First, there are values labeled '!$!0'. These should be substituted with NaN.

Furthermore, each sector1 value ends with a ten or eleven character string like '!$!49!$!EP'. Some sectors show up twice in the list like: 'Other Industry; Trade and Services!$!70!$!YZ', 'Other Industry; Trade and Services!$!63!$!YZ',

But it seems like those are actually the same sector. You'll need to remove everything past the exclamation point.

Many values in the sector1 variable start with the term '(Historic)'. Try removing that phrase as well.

replace() method¶

With pandas, you can use the replace() method to search for text and replace parts of a string with another string. If you know the exact string you're looking for, the replace() method is straight forward. For example, say you wanted to remove the string '(Trial)' from this data:

data
'(Trial) Banking'
'Banking'
'Farming'
'(Trial) Transportation'

You could use df['data'].replace('(Trial'), '') to replace (Trial) with an empty string.

regular expressions¶

What about this data?

data
'Other Industry; Trade and Services?$ab'
'Other Industry; Trade and Services?ceg'

This type of data is trickier. In this case, there's a pattern where you want to remove a string that starts with an exclamation point and then has an unknown number of characters after it. When you need to match patterns of character, you can use regular expressions.

The replace method can take a regular expression. So df['data'].replace('?.+', regex=True) where '?.+' means find a set of characters that starts with a question mark is then followed by one or more characters. You can see a regular expression cheat sheet here.

Fix these issues in the code cell below.

# TODO: In the sector1 variable, replace the string '!$10' with nan
#       Put the results back into the sector1 variable
# HINT: you can use the pandas replace() method and numpy.nan

sector['sector1'] = sector['sector1'].replace('!$10',np.nan)

# TODO: In the sector1 variable, remove the last 10 or 11 characters from the sector1 variable.
# HINT: There is more than one way to do this. To do it with one line of code,
# you can use the replace method with a regex expression '!.+'
# That regex expression looks for a string with an exclamation
# point followed by one or more characters

sector['sector1'] = sector['sector1'].replace('!.+','',regex=True)

# TODO: Remove the string '(Historic)' from the sector1 variable
# HINT: You can use the replace method

sector['sector1'] = sector['sector1'].replace(r'^\(Historic\)','',regex=True)

print('Number of unique sectors after cleaning:', len(list(sector['sector1'].unique())))
print('Percentage of null values after cleaning:', 100 * sector['sector1'].isnull().sum() / sector['sector1'].shape[0])

Number of unique sectors after cleaning: 156
Percentage of null values after cleaning: 0.0

sector['sector1'].value_counts()

Central Government (Central Agencies)            1644
Health                                            782
Social Protection                                 767
                                                  638
Irrigation and Drainage                           624
                                                 ... 
(Historic)Other fuels                               1
Compulsory pension and unemployment insurance       1
(Historic)Decentralization                          1
(Historic)Environment adjustment                    1
(Historic)Pensions and social insurance             1
Name: sector1, Length: 159, dtype: int64

Now there are 156 unique categorical values. That's better than 3060. If you were going to use this data with a supervised learning machine model, you could try converting these 156 values to dummy variables. You'd still have to train and test a model to see if those are good features.

You could try to consolidate similar categories together, which is what the challenge exercise in part 4 is about.

There are also still many entries with NaN values. How could you fill these in?

You might try to determine an appropriate category from the 'project_name' or 'lendinginstr' variables. If you make dummy variables including NaN values, then you could consider a feature with all zeros to represent NaN. Or you could delete these records from the data set. Pandas will ignore NaN values by default. That means, for a given row, all dummy variables will have a value of 0 if the sector1 value was NaN.

Don't forget about the bigger context! This data is being prepared for a machine learning algorithm. Whatever techniques you use to engineer new features, you'll need to use those when running your model on new data. So if your new data does not contain a sector1 value, you'll have to run whatever feature engineering processes you did on your training set.

Exercise 3¶

In this next exercise, use the pandas pd.get_dummies() method to create dummy variables. Then use the concat() method to concatenate the dummy variables to a dataframe that contains the project totalamt variable and the project year from the boardapprovaldate.

# TODO: Create dummy variables from the sector1 data. Put the results into a dataframe called dummies
# Hint: Use the get_dummies method
dummies = pd.get_dummies(sector['sector1'],drop_first = True)

# TODO: Create a new dataframe called df by 
#       filtering the projects data for the totalamt and
#       the year from boardapprovaldate
projects['year'] = projects['boardapprovaldate'].dt.year
df = projects[['totalamt','year']]

# TODO: Concatenate the results of dummies and projects
#       into a single data frame
df_final = pd.concat([df, dummies], axis = 1)

df_final.head()

You could continue to consolidate sector values using other techniques. For example, in the next exercise, you'll find categories with similar terms and then combine them together.

Keep in mind that how much to consolidate will depend on your machine learning model performance and your hardware's ability to handle the extra features in memory. If your hardware's memory can handle 3060 new features and your machine learning algorithm performs better, then go for it!

Exercise 4 (Challenge)¶

But can you do anything else with the sector1 variable?

The percentage of null values for 'sector1' is now 3.49%. That turns out to be the same number as the null values for the 'sector' column. You can see this if you scroll back up to where the code calculated the percentage of null values for each variable.

Perhaps the 'sector1' and 'sector' variable have the same information. If you look at the 'sector' variable, however, it also needs cleaning. The values look like this:

'Urban Transport;Urban Transport;Public Administration - Transportation'

It turns out the 'sector' variable combines information from the 'sector1' through 'sector5' variables and the 'mjsector' variable. Run the code cell below to look at the sector variable.

sector['sector']

0                                                      NaN
1                                                      NaN
2                                                      NaN
3                                                      NaN
4        Social Protection;Social Protection;Other Indu...
                               ...                        
18243                      (Historic)Hydro;(Historic)Hydro
18244    (Historic)Economic management;(Historic)Econom...
18245    (Historic)Economic management;(Historic)Econom...
18246    (Historic)Economic management;(Historic)Econom...
18247    (Historic)Economic management;(Historic)Econom...
Name: sector, Length: 18248, dtype: object

What else can you do? If you look at all of the diferent sector1 categories, it might be useful to combine a few of them together. For example, there are various categories with the term "Energy" in them. And then there are other categories that seem related to energy but don't have the word energy in them like "Thermal" and "Hydro". Some categories have the term "Renewable Energy", so perhaps you could make a separate "Renewable Energy" category.

Similarly, there are categories with the term "Transportation" in them, and then there are related categories like "Highways".

In the next cell, find all sector1 values with the term 'Energy' in them. For each of these rows, put the string 'energy' in a new column called 'sector1_aggregates'. Do the same for "Transportation".

import re

# Create the sector1_aggregates variable
sector['sector1_aggregates'] = sector['sector1']

# TODO: The code above created a new variable called sector1_aggregates. 
#       Currently, sector1_aggregates has all of the same values as sector1
#       For this task, find all the rows in sector1_aggregates with the term 'Energy' in them, 
#       For all of these rows, replace whatever is the value is with the term 'Energy'.
#       The idea is to simplify the category names by combining various categories together.
#       Then, do the same for the term 'Transportation
# HINT: You can use the contains() methods. See the documentation for how to ignore case using the re library
# HINT: You might get an error saying "cannot index with vector containing NA / NaN values." 
#       Try converting NaN values to something else like False or a string

sector.loc[sector['sector1_aggregates'].str.contains('Energy')|sector['sector1_aggregates'].str.contains('energy'),'sector1_aggregates'] = 'Energy'
sector.loc[sector['sector1_aggregates'].str.contains('Transportation')|sector['sector1_aggregates'].str.contains('transportation'),'sector1_aggregates'] = 'Transportation'
print('Number of unique sectors after cleaning:', len(list(sector['sector1_aggregates'].unique())))

Number of unique sectors after cleaning: 140

import re

# Create the sector1_aggregates variable
sector.loc[:,'sector1_aggregates'] = sector['sector1']

# TODO: The code above created a new variable called sector1_aggregates. 
#       Currently, sector1_aggregates has all of the same values as sector1
#       For this task, find all the rows in sector1_aggregates with the term 'Energy' in them, 
#       For all of these rows, replace whatever is the value is with the term 'Energy'.
#       The idea is to simplify the category names by combining various categories together.
#       Then, do the same for the term 'Transportation
# HINT: You can use the contains() methods. See the documentation for how to ignore case using the re library
# HINT: You might get an error saying "cannot index with vector containing NA / NaN values." 
#       Try converting NaN values to something else like False or a string

sector.loc[sector['sector1_aggregates'].str.contains('Energy', flags=re.IGNORECASE),'sector1_aggregates'] = 'Energy'
sector.loc[sector['sector1_aggregates'].str.contains('Transportation', flags=re.IGNORECASE),'sector1_aggregates'] = 'Transportation'

print('Number of unique sectors after cleaning:', len(list(sector['sector1_aggregates'].unique())))

Number of unique sectors after cleaning: 140

Conclusion¶

Pandas makes it relatively easy to create dummy variables; however, oftentimes you'll need to clean the data first.

	boardapprovaldate	board_approval_month	closingdate
0	2018-06-28T00:00:00Z	June	NaN
1	2018-06-28T00:00:00Z	June	2023-12-31T00:00:00Z
2	2018-06-28T00:00:00Z	June	NaN
3	2018-06-27T00:00:00Z	June	2023-06-28T00:00:00Z
4	2018-06-27T00:00:00Z	June	2023-05-31T00:00:00Z
5	2018-06-27T00:00:00Z	June	2019-12-31T00:00:00Z
6	2018-06-27T00:00:00Z	June	2023-10-31T00:00:00Z
7	2018-06-27T00:00:00Z	June	2023-12-31T00:00:00Z
8	2018-06-27T00:00:00Z	June	2022-12-31T00:00:00Z
9	2018-06-27T00:00:00Z	June	2023-12-31T00:00:00Z
10	2018-06-27T00:00:00Z	June	2023-12-31T00:00:00Z
11	2018-06-27T00:00:00Z	June	NaN
12	2018-06-27T00:00:00Z	June	NaN
13	2018-06-27T00:00:00Z	June	NaN
14	2018-06-27T00:00:00Z	June	NaN

	id	regionname	countryname	prodline	lendinginstr	lendinginstrtype	envassesmentcategorycode	supplementprojectflg	productlinetype	projectstatusdisplay	...	mjtheme3name	mjtheme4name	mjtheme5name	location	GeoLocID	GeoLocName	Latitude	Longitude	Country	Unnamed: 56
0	P162228	Other	World;World	RE	Investment Project Financing	IN	C	N	L	Active	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	P163962	Africa	Democratic Republic of the Congo;Democratic Re...	PE	Investment Project Financing	IN	B	N	L	Active	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	P167672	South Asia	People's Republic of Bangladesh;People's Repub...	PE	Investment Project Financing	IN	NaN	Y	L	Active	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	P158768	South Asia	Islamic Republic of Afghanistan;Islamic Repu...	PE	Investment Project Financing	IN	A	N	L	Active	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	P161364	Africa	Federal Republic of Nigeria;Federal Republic o...	PE	Investment Project Financing	IN	B	N	L	Active	...	NaN	NaN	NaN	0002327546!$!Ogun State!$!7!$!3.58333!$!NG;000...	0002327546;0002328925;0002565340;0002565343;00...	Ogun State;Niger State;Abia State;Edo;Kebbi St...	7;10;5.41667;6.5;11.5;8	3.58333;6;7.5;6;4;10.5	NG;NG;NG;NG;NG;NG	NaN

	Country Name	Country Code	Indicator Name	Indicator Code	1960	1961	1962	1963	1964	1965	1966	1967	1968	1969	1970	1971	1972	1973	1974	1975	1976	1977	1978	1979	1980	1981	1982	1983	1984	1985	1986	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	Unnamed: 62
0	Aruba	ABW	Population, total	SP.POP.TOTL	54211.0	55438.0	56225.0	56695.0	57032.0	57360.0	57715.0	58055.0	58386.0	58726.0	59063.0	59440.0	59840.0	60243.0	60528.0	60657.0	60586.0	60366.0	60103.0	59980.0	60096.0	60567.0	61345.0	62201.0	62836.0	63026.0	62644.0	61833.0	61079.0	61032.0	62149.0	64622.0	68235.0	72504.0	76700.0	80324.0	83200.0	85451.0	87277.0	89005.0	90853.0	92898.0	94992.0	97017.0	98737.0	100031.0	100832.0	101220.0	101353.0	101453.0	101669.0	102053.0	102577.0	103187.0	103795.0	104341.0	104822.0	105264.0	NaN
1	Afghanistan	AFG	Population, total	SP.POP.TOTL	8996351.0	9166764.0	9345868.0	9533954.0	9731361.0	9938414.0	10152331.0	10372630.0	10604346.0	10854428.0	11126123.0	11417825.0	11721940.0	12027822.0	12321541.0	12590286.0	12840299.0	13067538.0	13237734.0	13306695.0	13248370.0	13053954.0	12749645.0	12389269.0	12047115.0	11783050.0	11601041.0	11502761.0	11540888.0	11777609.0	12249114.0	12993657.0	13981231.0	15095099.0	16172719.0	17099541.0	17822884.0	18381605.0	18863999.0	19403676.0	20093756.0	20966463.0	21979923.0	23064851.0	24118979.0	25070798.0	25893450.0	26616792.0	27294031.0	28004331.0	28803167.0	29708599.0	30696958.0	31731688.0	32758020.0	33736494.0	34656032.0	35530081.0	NaN
2	Angola	AGO	Population, total	SP.POP.TOTL	5643182.0	5753024.0	5866061.0	5980417.0	6093321.0	6203299.0	6309770.0	6414995.0	6523791.0	6642632.0	6776381.0	6927269.0	7094834.0	7277960.0	7474338.0	7682479.0	7900997.0	8130988.0	8376147.0	8641521.0	8929900.0	9244507.0	9582156.0	9931562.0	10277321.0	10609042.0	10921037.0	11218268.0	11513968.0	11827237.0	12171441.0	12553446.0	12968345.0	13403734.0	13841301.0	14268994.0	14682284.0	15088981.0	15504318.0	15949766.0	16440924.0	16983266.0	17572649.0	18203369.0	18865716.0	19552542.0	20262399.0	20997687.0	21759420.0	22549547.0	23369131.0	24218565.0	25096150.0	25998340.0	26920466.0	27859305.0	28813463.0	29784193.0	NaN
3	Albania	ALB	Population, total	SP.POP.TOTL	1608800.0	1659800.0	1711319.0	1762621.0	1814135.0	1864791.0	1914573.0	1965598.0	2022272.0	2081695.0	2135479.0	2187853.0	2243126.0	2296752.0	2350124.0	2404831.0	2458526.0	2513546.0	2566266.0	2617832.0	2671997.0	2726056.0	2784278.0	2843960.0	2904429.0	2964762.0	3022635.0	3083605.0	3142336.0	3227943.0	3286542.0	3266790.0	3247039.0	3227287.0	3207536.0	3187784.0	3168033.0	3148281.0	3128530.0	3108778.0	3089027.0	3060173.0	3051010.0	3039616.0	3026939.0	3011487.0	2992547.0	2970017.0	2947314.0	2927519.0	2913021.0	2905195.0	2900401.0	2895092.0	2889104.0	2880703.0	2876101.0	2873457.0	NaN
4	Andorra	AND	Population, total	SP.POP.TOTL	13411.0	14375.0	15370.0	16412.0	17469.0	18549.0	19647.0	20758.0	21890.0	23058.0	24276.0	25559.0	26892.0	28232.0	29520.0	30705.0	31777.0	32771.0	33737.0	34818.0	36067.0	37500.0	39114.0	40867.0	42706.0	44600.0	46517.0	48455.0	50434.0	52448.0	54509.0	56671.0	58888.0	60971.0	62677.0	63850.0	64360.0	64327.0	64142.0	64370.0	65390.0	67341.0	70049.0	73182.0	76244.0	78867.0	80991.0	82683.0	83861.0	84462.0	84449.0	83751.0	82431.0	80788.0	79223.0	78014.0	77281.0	76965.0	NaN

	Country Name	Country Code	Indicator Name	Indicator Code	1960	1961	1962	1963	1964	1965	1966	1967	1968	1969	1970	1971	1972	1973	1974	1975	1976	1977	1978	1979	1980	1981	1982	1983	1984	1985	1986	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017
67	Eritrea	ERI	Population, total	SP.POP.TOTL	1397491.0	1432640.0	1469645.0	1508273.0	1548187.0	1589179.0	1631147.0	1674204.0	1718525.0	1764343.0	1811878.0	1861199.0	1912302.0	1965160.0	2019717.0	2075965.0	2133723.0	2193068.0	2254450.0	2318495.0	2385540.0	2454766.0	2525521.0	2598410.0	2674289.0	2753151.0	2837111.0	2924349.0	3006361.0	3071771.0	3113311.0	3127297.0	3118582.0	3099047.0	3085443.0	3090159.0	3116379.0	3161350.0	3224223.0	3302263.0	3392801.0	3497124.0	3614639.0	3738265.0	3858623.0	3969007.0	4066648.0	4153332.0	4232636.0	4310334.0	4390840.0	4474690.0	NaN	NaN	NaN	NaN	NaN	NaN
108	Not classified	INX	Population, total	SP.POP.TOTL	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
125	Kuwait	KWT	Population, total	SP.POP.TOTL	269618.0	301336.0	338296.0	379891.0	425235.0	473554.0	524856.0	579007.0	634897.0	691129.0	746767.0	801142.0	854604.0	908520.0	964834.0	1024940.0	1089209.0	1157033.0	1227601.0	1299683.0	1372318.0	1442991.0	1511314.0	1580638.0	1655833.0	1738994.0	1836105.0	1942810.0	2038885.0	2096932.0	2099615.0	2035661.0	NaN	NaN	NaN	1610651.0	1631740.0	1715314.0	1836353.0	1957066.0	2050741.0	2109355.0	2143833.0	2169118.0	2207939.0	2276623.0	2377258.0	2503410.0	2652340.0	2818939.0	2998083.0	3191051.0	3395556.0	3598385.0	3782450.0	3935794.0	4052584.0	4136528.0
194	West Bank and Gaza	PSE	Population, total	SP.POP.TOTL	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1978248.0	2068845.0	2163591.0	2262676.0	2366298.0	2474666.0	2587997.0	2706518.0	2776568.0	2848431.0	2922153.0	2997784.0	3075373.0	3154969.0	3236626.0	3320396.0	3406334.0	3494496.0	3596688.0	3702218.0	3811102.0	3927051.0	4046901.0	4169506.0	4294682.0	4422143.0	4551566.0	4684777.0
212	Serbia	SRB	Population, total	SP.POP.TOTL	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	7586000.0	7595636.0	7646424.0	7699307.0	7734639.0	7625357.0	7617794.0	7596501.0	7567745.0	7540401.0	7516346.0	7503433.0	7496522.0	7480591.0	7463157.0	7440769.0	7411569.0	7381579.0	7350222.0	7320807.0	7291436.0	7234099.0	7199077.0	7164132.0	7130576.0	7095383.0	7058322.0	7022268.0
223	Sint Maarten (Dutch part)	SXM	Population, total	SP.POP.TOTL	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	31240.0	31084.0	30519.0	30600.0	30777.0	31472.0	32488.0	33011.0	33441.0	33811.0	33964.0	34238.0	34056.0	33435.0	34640.0	36607.0	37685.0	38824.0	39969.0	41109.0

	Country_Name	Country_Code	1960
0	Aruba	ABW	5.421100e+04
1	Afghanistan	AFG	8.996351e+06
2	Angola	AGO	5.643182e+06
3	Albania	ALB	1.608800e+06
4	Andorra	AND	1.341100e+04
5	Arab World	ARB	9.249093e+07
6	United Arab Emirates	ARE	9.263400e+04
7	Argentina	ARG	2.061908e+07
8	Armenia	ARM	1.874120e+06
9	American Samoa	ASM	2.001300e+04
10	Antigua and Barbuda	ATG	5.533900e+04
11	Australia	AUS	1.027648e+07
12	Austria	AUT	7.047539e+06
13	Azerbaijan	AZE	3.895396e+06
14	Burundi	BDI	2.786106e+06
15	Belgium	BEL	9.153489e+06
16	Benin	BEN	2.431622e+06
17	Burkina Faso	BFA	4.829288e+06
18	Bangladesh	BGD	4.819975e+07
19	Bulgaria	BGR	7.867374e+06
20	Bahrain	BHR	1.624270e+05
21	Bahamas, The	BHS	1.095280e+05
22	Bosnia and Herzegovina	BIH	3.225668e+06
23	Belarus	BLR	8.198000e+06
24	Belize	BLZ	9.206400e+04
25	Bermuda	BMU	4.440000e+04
26	Bolivia	BOL	3.693449e+06
27	Brazil	BRA	7.220755e+07
28	Barbados	BRB	2.309390e+05
29	Brunei Darussalam	BRN	8.174500e+04
30	Bhutan	BTN	2.232880e+05
31	Botswana	BWA	5.245520e+05
32	Central African Republic	CAF	1.503508e+06
33	Canada	CAN	1.790901e+07
34	Central Europe and the Baltics	CEB	9.140158e+07
35	Switzerland	CHE	5.327827e+06
36	Channel Islands	CHI	1.094200e+05
37	Chile	CHL	7.716625e+06
38	China	CHN	6.670700e+08
39	Cote d'Ivoire	CIV	3.558988e+06
40	Cameroon	CMR	5.176268e+06
41	Congo, Dem. Rep.	COD	1.524825e+07
42	Congo, Rep.	COG	1.037220e+06
43	Colombia	COL	1.648038e+07
44	Comoros	COM	1.911210e+05
45	Cabo Verde	CPV	2.023100e+05
46	Costa Rica	CRI	1.333040e+06
47	Caribbean small states	CSS	4.198307e+06
48	Cuba	CUB	7.141135e+06
49	Curacao	CUW	1.248260e+05
50	Cayman Islands	CYM	7.865000e+03
51	Cyprus	CYP	5.729300e+05
52	Czech Republic	CZE	9.602006e+06
53	Germany	DEU	7.281490e+07
54	Djibouti	DJI	8.363600e+04
55	Dominica	DMA	6.001100e+04
56	Denmark	DNK	4.579603e+06
57	Dominican Republic	DOM	3.294042e+06
58	Algeria	DZA	1.112489e+07
59	East Asia & Pacific (excluding high income)	EAP	8.939563e+08
60	Early-demographic dividend	EAR	9.792874e+08
61	East Asia & Pacific	EAS	1.039945e+09
62	Europe & Central Asia (excluding high income)	ECA	2.749479e+08
63	Europe & Central Asia	ECS	6.672464e+08
64	Ecuador	ECU	4.545550e+06
65	Egypt, Arab Rep.	EGY	2.699653e+07
66	Euro area	EMU	2.653965e+08
67	Eritrea	ERI	1.397491e+06
68	Spain	ESP	3.045500e+07
69	Estonia	EST	1.211537e+06
70	Ethiopia	ETH	2.215128e+07
71	European Union	EUU	4.094985e+08
72	Fragile and conflict affected situations	FCS	1.199679e+08
73	Finland	FIN	4.429634e+06
74	Fiji	FJI	3.933860e+05
75	France	FRA	4.681424e+07
76	Faroe Islands	FRO	3.466100e+04
77	Micronesia, Fed. Sts.	FSM	4.453700e+04
78	Gabon	GAB	4.991840e+05
79	United Kingdom	GBR	5.240000e+07
80	Georgia	GEO	3.645600e+06
81	Ghana	GHA	6.652287e+06
82	Gibraltar	GIB	2.339400e+04
83	Guinea	GIN	3.577409e+06
84	Gambia, The	GMB	3.679280e+05
85	Guinea-Bissau	GNB	6.164090e+05
86	Equatorial Guinea	GNQ	2.553230e+05
87	Greece	GRC	8.331725e+06
88	Grenada	GRD	8.986900e+04
89	Greenland	GRL	3.250000e+04
90	Guatemala	GTM	4.210747e+06
91	Guam	GUM	6.674200e+04
92	Guyana	GUY	5.718190e+05
93	High income	HIC	7.805019e+08
94	Hong Kong SAR, China	HKG	3.075605e+06
95	Honduras	HND	2.038637e+06
96	Heavily indebted poor countries (HIPC)	HPC	1.624956e+08
97	Croatia	HRV	4.140000e+06
98	Haiti	HTI	3.866159e+06
99	Hungary	HUN	9.983967e+06
100	IBRD only	IBD	1.917374e+09
101	IDA & IBRD total	IBT	2.299864e+09
102	IDA total	IDA	3.824897e+08
103	IDA blend	IDB	1.231951e+08
104	Indonesia	IDN	8.779252e+07
105	IDA only	IDX	2.592947e+08
106	Isle of Man	IMN	4.844200e+04
107	India	IND	4.494806e+08
108	Not classified	INX	NaN
109	Ireland	IRL	2.828600e+06
110	Iran, Islamic Rep.	IRN	2.190690e+07
111	Iraq	IRQ	7.289761e+06
112	Iceland	ISL	1.755740e+05
113	Israel	ISR	2.114020e+06
114	Italy	ITA	5.019970e+07
115	Jamaica	JAM	1.628252e+06
116	Jordan	JOR	9.322570e+05
117	Japan	JPN	9.250057e+07
118	Kazakhstan	KAZ	9.714260e+06
119	Kenya	KEN	8.105440e+06
120	Kyrgyz Republic	KGZ	2.172300e+06
121	Cambodia	KHM	5.722370e+06
122	Kiribati	KIR	4.123300e+04
123	St. Kitts and Nevis	KNA	5.119500e+04
124	Korea, Rep.	KOR	2.501237e+07
125	Kuwait	KWT	2.696180e+05
126	Latin America & Caribbean (excluding high income)	LAC	1.845365e+08
127	Lao PDR	LAO	2.120896e+06
128	Lebanon	LBN	1.804926e+06
129	Liberia	LBR	1.120313e+06
130	Libya	LBY	1.448417e+06
131	St. Lucia	LCA	8.989700e+04
132	Latin America & Caribbean	LCN	2.204347e+08
133	Least developed countries: UN classification	LDC	2.407422e+08
134	Low income	LIC	1.665028e+08
135	Liechtenstein	LIE	1.649500e+04
136	Sri Lanka	LKA	9.874481e+06
137	Lower middle income	LMC	9.345867e+08
138	Low & middle income	LMY	2.251658e+09
139	Lesotho	LSO	8.515910e+05
140	Late-demographic dividend	LTE	1.097221e+09
141	Lithuania	LTU	2.778550e+06
142	Luxembourg	LUX	3.139700e+05
143	Latvia	LVA	2.120979e+06
144	Macao SAR, China	MAC	1.677960e+05
145	St. Martin (French part)	MAF	4.279000e+03
146	Morocco	MAR	1.232853e+07
147	Monaco	MCO	2.245200e+04
148	Moldova	MDA	2.544000e+06
149	Madagascar	MDG	5.099373e+06
150	Maldives	MDV	8.988700e+04
151	Middle East & North Africa	MEA	1.054887e+08
152	Mexico	MEX	3.817411e+07
153	Marshall Islands	MHL	1.466200e+04
154	Middle income	MIC	2.085156e+09
155	Macedonia, FYR	MKD	1.488667e+06
156	Mali	MLI	5.263733e+06
157	Malta	MLT	3.265500e+05
158	Myanmar	MMR	2.098612e+07
159	Middle East & North Africa (excluding high inc...	MNA	9.783777e+07
160	Montenegro	MNE	4.805790e+05
161	Mongolia	MNG	9.555050e+05
162	Northern Mariana Islands	MNP	1.003500e+04
163	Mozambique	MOZ	7.388695e+06
164	Mauritania	MRT	8.581680e+05
165	Mauritius	MUS	6.593510e+05
166	Malawi	MWI	3.618595e+06
167	Malaysia	MYS	8.157106e+06
168	North America	NAC	1.986244e+08
169	Namibia	NAM	6.025440e+05
170	New Caledonia	NCL	7.900000e+04
171	Niger	NER	3.388764e+06
172	Nigeria	NGA	4.513781e+07
173	Nicaragua	NIC	1.774699e+06
174	Netherlands	NLD	1.148663e+07
175	Norway	NOR	3.581239e+06
176	Nepal	NPL	1.006301e+07
177	Nauru	NRU	4.433000e+03
178	New Zealand	NZL	2.371800e+06
179	OECD members	OED	7.887091e+08
180	Oman	OMN	5.517400e+05
181	Other small states	OSS	9.196324e+06
182	Pakistan	PAK	4.490829e+07
183	Panama	PAN	1.132921e+06
184	Peru	PER	1.006152e+07
185	Philippines	PHL	2.627302e+07
186	Palau	PLW	9.642000e+03
187	Papua New Guinea	PNG	2.010677e+06
188	Poland	POL	2.963745e+07
189	Pre-demographic dividend	PRE	1.886362e+08
190	Puerto Rico	PRI	2.358000e+06
191	Korea, Dem. People’s Rep.	PRK	1.142418e+07
192	Portugal	PRT	8.857716e+06
193	Paraguay	PRY	1.902875e+06
194	West Bank and Gaza	PSE	NaN
195	Pacific island small states	PSS	8.658090e+05
196	Post-demographic dividend	PST	7.547053e+08
197	French Polynesia	PYF	7.807600e+04
198	Qatar	QAT	4.738400e+04
199	Romania	ROU	1.840690e+07
200	Russian Federation	RUS	1.198970e+08
201	Rwanda	RWA	2.933428e+06
202	South Asia	SAS	5.718357e+08
203	Saudi Arabia	SAU	4.086539e+06
204	Sudan	SDN	7.544491e+06
205	Senegal	SEN	3.206749e+06
206	Singapore	SGP	1.646400e+06
207	Solomon Islands	SLB	1.178660e+05
208	Sierra Leone	SLE	2.297110e+06
209	El Salvador	SLV	2.762899e+06
210	San Marino	SMR	1.539700e+04
211	Somalia	SOM	2.755947e+06
212	Serbia	SRB	NaN
213	Sub-Saharan Africa (excluding high income)	SSA	2.285443e+08
214	South Sudan	SSD	2.955152e+06
215	Sub-Saharan Africa	SSF	2.285860e+08
216	Small states	SST	1.426044e+07
217	Sao Tome and Principe	STP	6.425300e+04
218	Suriname	SUR	2.899660e+05
219	Slovak Republic	SVK	4.068095e+06
220	Slovenia	SVN	1.584720e+06
221	Sweden	SWE	7.484656e+06
222	Swaziland	SWZ	3.491740e+05
223	Sint Maarten (Dutch part)	SXM	NaN
224	Seychelles	SYC	4.170000e+04
225	Syrian Arab Republic	SYR	4.573512e+06
226	Turks and Caicos Islands	TCA	5.726000e+03
227	Chad	TCD	3.001593e+06
228	East Asia & Pacific (IDA & IBRD countries)	TEA	8.825218e+08
229	Europe & Central Asia (IDA & IBRD countries)	TEC	3.087254e+08
230	Togo	TGO	1.580513e+06
231	Thailand	THA	2.739718e+07
232	Tajikistan	TJK	2.087038e+06
233	Turkmenistan	TKM	1.603258e+06
234	Latin America & the Caribbean (IDA & IBRD coun...	TLA	2.103576e+08
235	Timor-Leste	TLS	4.999500e+05
236	Middle East & North Africa (IDA & IBRD countries)	TMN	9.783777e+07
237	Tonga	TON	6.160100e+04
238	South Asia (IDA & IBRD)	TSA	5.718357e+08
239	Sub-Saharan Africa (IDA & IBRD countries)	TSS	2.285860e+08
240	Trinidad and Tobago	TTO	8.484790e+05
241	Tunisia	TUN	4.176266e+06
242	Turkey	TUR	2.747233e+07
243	Tuvalu	TUV	6.104000e+03
244	Tanzania	TZA	1.007451e+07
245	Uganda	UGA	6.788214e+06
246	Ukraine	UKR	4.266215e+07
247	Upper middle income	UMC	1.150569e+09
248	Uruguay	URY	2.538651e+06
249	United States	USA	1.806710e+08
250	Uzbekistan	UZB	8.549493e+06
251	St. Vincent and the Grenadines	VCT	8.094900e+04
252	Venezuela, RB	VEN	8.146847e+06
253	British Virgin Islands	VGB	8.033000e+03
254	Virgin Islands (U.S.)	VIR	3.250000e+04
255	Vietnam	VNM	3.267063e+07
256	Vanuatu	VUT	6.369900e+04
257	World	WLD	3.032160e+09
258	Samoa	WSM	1.086460e+05
259	Kosovo	XKX	9.470000e+05
260	Yemen, Rep.	YEM	5.172135e+06
261	South Africa	ZAF	1.745686e+07
262	Zambia	ZMB	3.044846e+06
263	Zimbabwe	ZWE	3.747369e+06

	indicator	country	countryiso3code	date	value
0	{'id': 'SP.RUR.TOTL', 'value': 'Rural populati...	{'id': 'CH', 'value': 'Switzerland'}	CHE	2001	1924949
1	{'id': 'SP.RUR.TOTL', 'value': 'Rural populati...	{'id': 'CH', 'value': 'Switzerland'}	CHE	2000	1912232
2	{'id': 'SP.RUR.TOTL', 'value': 'Rural populati...	{'id': 'CH', 'value': 'Switzerland'}	CHE	1999	1897587
3	{'id': 'SP.RUR.TOTL', 'value': 'Rural populati...	{'id': 'CH', 'value': 'Switzerland'}	CHE	1998	1884719
4	{'id': 'SP.RUR.TOTL', 'value': 'Rural populati...	{'id': 'CH', 'value': 'Switzerland'}	CHE	1997	1875299
5	{'id': 'SP.RUR.TOTL', 'value': 'Rural populati...	{'id': 'CH', 'value': 'Switzerland'}	CHE	1996	1866968
6	{'id': 'SP.RUR.TOTL', 'value': 'Rural populati...	{'id': 'CH', 'value': 'Switzerland'}	CHE	1995	1854939

	totalamt	lendprojectcost
0	0	500,000
1	200,000,000	200,000,000
2	58,330,000	50,000,000
3	20,000,000	50,000,000
4	100,000,000	100,000,000

	Country Name	Country Code	Indicator Name	Indicator Code	1960	1961	1962	1963	1964	1965	...	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017
0	Aruba	ABW	GDP (current US$)	NY.GDP.MKTP.CD	NaN	NaN	NaN	NaN	NaN	NaN	...	2.791961e+09	2.498933e+09	2.467704e+09	2.584464e+09	NaN	NaN	NaN	NaN	NaN	NaN
1	Afghanistan	AFG	GDP (current US$)	NY.GDP.MKTP.CD	5.377778e+08	5.488889e+08	5.466667e+08	7.511112e+08	8.000000e+08	1.006667e+09	...	1.019053e+10	1.248694e+10	1.593680e+10	1.793024e+10	2.053654e+10	2.026425e+10	2.061610e+10	1.921556e+10	1.946902e+10	2.081530e+10
2	Angola	AGO	GDP (current US$)	NY.GDP.MKTP.CD	NaN	NaN	NaN	NaN	NaN	NaN	...	8.417804e+10	7.549239e+10	8.252614e+10	1.041158e+11	1.139232e+11	1.249125e+11	1.267302e+11	1.026212e+11	9.533720e+10	1.242094e+11
3	Albania	ALB	GDP (current US$)	NY.GDP.MKTP.CD	NaN	NaN	NaN	NaN	NaN	NaN	...	1.288135e+10	1.204421e+10	1.192695e+10	1.289087e+10	1.231978e+10	1.277628e+10	1.322824e+10	1.138693e+10	1.188368e+10	1.303935e+10
4	Andorra	AND	GDP (current US$)	NY.GDP.MKTP.CD	NaN	NaN	NaN	NaN	NaN	NaN	...	4.007353e+09	3.660531e+09	3.355695e+09	3.442063e+09	3.164615e+09	3.281585e+09	3.350736e+09	2.811489e+09	2.877312e+09	3.012914e+09

	id	regionname	countryname	prodline	lendinginstr	lendinginstrtype	envassesmentcategorycode	supplementprojectflg	productlinetype	projectstatusdisplay	status	project_name	boardapprovaldate	board_approval_month	closingdate	lendprojectcost	ibrdcommamt	idacommamt	totalamt	grantamt	borrower	impagency	url	projectdoc	majorsector_percent	sector1	sector2	sector3	sector4	sector5	sector	mjsector1	mjsector2	mjsector3	mjsector4	mjsector5	mjsector	theme1	theme2	theme3	theme4	theme5	theme	goal	financier	mjtheme1name	mjtheme2name	mjtheme3name	mjtheme4name	mjtheme5name	location	GeoLocID	GeoLocName	Latitude	Longitude	Country
11166	P009285	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	PE	Specific Investment Loan	IN	A	N	L	Closed	Closed	Kolubara B Thermal Power & Lignite Mine Project	1991-06-25 00:00:00+00:00	June	1997-06-30T00:00:00Z	300,000,000	300,000,000	0	300000000	0	EPS ROA (ZEP)	EPS	http://projects.worldbank.org/P009285/kolubara...	NaN	NaN	Power!$!44!$!LD	Renewable Energy Biomass!$!56!$!LB	NaN	NaN	NaN	Power;Power;Renewable Energy Biomass	NaN	NaN	NaN	NaN	NaN	Energy and Extractives;Energy and Extractives;...	Rural services and infrastructure!$!33!$!78	Infrastructure services for private sector dev...	Urban services and housing for the poor!$!33!$!71	NaN	NaN	NaN	Corporate Advocacy Priorities;Corporate Advoca...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
11410	P009231	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	PE	Sector Investment and Maintenance Loan	IN	B	N	L	Closed	Closed	Highway Sector Loan Project (03)	1990-06-20 00:00:00+00:00	June	1994-12-31T00:00:00Z	292,000,000	292,000,000	0	292000000	0	ALL SIX ROAD ORGANIZATIONS	FARP	http://projects.worldbank.org/P009231/highway-...	NaN	NaN	Roads and highways!$!100!$!TA	NaN	NaN	NaN	NaN	Roads and highways;Roads and highways	NaN	NaN	NaN	NaN	NaN	Transportation;Transportation	Export development and competitiveness!$!25!$!45	Pollution management and environmental health!...	Infrastructure services for private sector dev...	NaN	NaN	NaN	Global Public Goods Priorities\|Corporate Advoc...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
11479	P009219	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	PE	Structural Adjustment Loan	AD	C	N	L	Closed	Closed	Structural Adjustment Loan Project (02)	1990-04-12 00:00:00+00:00	April	1991-09-30T00:00:00Z	400,000,000	400,000,000	0	400000000	0	NBY	NBY	http://projects.worldbank.org/P009219/structur...	NaN	NaN	Other Industry; Trade and Services!$!38!$!YZ	Banking Institutions!$!36!$!FA	Central Government (Central Agencies)!$!12!$!BC	Other industry!$!7!$!YW	Other social services!$!7!$!JB	Other Industry; Trade and Services;Other Indus...	NaN	NaN	NaN	NaN	NaN	Industry; Trade and Services;Industry; Trade a...	Macroeconomic management!$!33!$!23	International financial standards and systems!...	Other public sector governance!$!17!$!30	Trade facilitation and market access!$!16!$!49	Other economic management!$!17!$!24	NaN	Corporate Advocacy Priorities;Corporate Advoca...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
11694	P009225	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	PE	Sector Investment and Maintenance Loan	IN	C	N	L	Closed	Closed	Railway Project (07)	1989-05-23 00:00:00+00:00	May	1992-12-31T00:00:00Z	138,000,000	138,000,000	0	138000000	0	RTO BELGRADE; RE LJUBLJANA; RTO NOVI SAD	BORROWER	http://projects.worldbank.org/P009225/railway-...	NaN	NaN	Railways!$!100!$!TW	NaN	NaN	NaN	NaN	Railways;Railways	NaN	NaN	NaN	NaN	NaN	Transportation;Transportation	!$!0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
11695	P009275	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	PE	Specific Investment Loan	IN	NaN	N	L	Closed	Closed	Istria Water Supply & Sewerage Project	1989-05-23 00:00:00+00:00	May	1997-12-31T00:00:00Z	60,000,000	60,000,000	0	60000000	0	RIZANA WW; ISTRIAN WW & PULA WW	BUTONIGA WW AND RIZANA WW	http://projects.worldbank.org/P009275/istria-w...	NaN	NaN	(Historic)Urban water supply!$!100!$!WU	NaN	NaN	NaN	NaN	(Historic)Urban water supply;(Historic)Urban w...	NaN	NaN	NaN	NaN	NaN	Water; Sanitation and Waste Management;Water; ...	!$!0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
17903	P009137	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	PE	Specific Investment Loan	IN	NaN	N	L	Closed	Closed	Electric Power Project	1962-07-11 00:00:00+00:00	July	1968-12-31T00:00:00Z	30,000,000	30,000,000	0	30000000	0	NaN	NaN	http://projects.worldbank.org/P009137/electric...	NaN	NaN	(Historic)Hydro!$!100!$!PH	NaN	NaN	NaN	NaN	(Historic)Hydro;(Historic)Hydro	NaN	NaN	NaN	NaN	NaN	(Historic)Electric Power & Other Energy;(Histo...	!$!0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
17963	P009136	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	PE	Specific Investment Loan	IN	NaN	N	L	Closed	Closed	Electric Power Project	1961-02-23 00:00:00+00:00	February	1968-01-31T00:00:00Z	30,000,000	30,000,000	0	30000000	0	NaN	NaN	http://projects.worldbank.org/P009136/electric...	NaN	NaN	(Historic)Hydro!$!100!$!PH	NaN	NaN	NaN	NaN	(Historic)Hydro;(Historic)Hydro	NaN	NaN	NaN	NaN	NaN	(Historic)Electric Power & Other Energy;(Histo...	!$!0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
18175	P009135	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	PE	Structural Adjustment Loan	AD	NaN	N	L	Closed	Closed	Power Mining Industry Project	1953-02-11 00:00:00+00:00	February	1957-12-31T00:00:00Z	30,000,000	30,000,000	0	30000000	0	NaN	NaN	http://projects.worldbank.org/P009135/power-mi...	NaN	NaN	(Historic)Economic management!$!100!$!ME	NaN	NaN	NaN	NaN	(Historic)Economic management;(Historic)Econom...	NaN	NaN	NaN	NaN	NaN	(Historic)Multisector;(Historic)Multisector	!$!0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
18197	P009134	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	PE	Structural Adjustment Loan	AD	NaN	N	L	Closed	Closed	Power Mining Industry Project	1951-10-11 00:00:00+00:00	October	1957-12-31T00:00:00Z	28,000,000	28,000,000	0	28000000	0	NaN	NaN	http://projects.worldbank.org/P009134/power-mi...	NaN	NaN	(Historic)Economic management!$!100!$!ME	NaN	NaN	NaN	NaN	(Historic)Economic management;(Historic)Econom...	NaN	NaN	NaN	NaN	NaN	(Historic)Multisector;(Historic)Multisector	!$!0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
18228	P009133	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	PE	Specific Investment Loan	IN	NaN	N	L	Closed	Closed	Agriculture Timber Equipment Project	1949-10-17 00:00:00+00:00	October	1950-12-31T00:00:00Z	2,700,000	2,700,000	0	2700000	0	NaN	NaN	http://projects.worldbank.org/P009133/agricult...	NaN	NaN	Forestry!$!100!$!AT	NaN	NaN	NaN	NaN	Forestry;Forestry	NaN	NaN	NaN	NaN	NaN	Agriculture; Fishing and Forestry;Agriculture;...	!$!0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	regionname	countryname	lendinginstr	totalamt	boardapprovaldate	location	GeoLocID	GeoLocName	Latitude	Longitude	Country	project_name
13973	Europe and Central Asia	Macedonia	Specific Investment Loan	24000000	1980-02-01 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Agriculture & Agroindustry 2 Project (Macedonia)
13048	Europe and Central Asia	Bosnia and Herzegovina	Specific Investment Loan	0	1983-07-26 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	POWER TRANS.III
13049	Europe and Central Asia	Republic of Croatia	Specific Investment Loan	0	1983-07-26 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	POWER TRANS.III
13050	Europe and Central Asia	Macedonia	Specific Investment Loan	0	1983-07-26 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	POWER TRANS.III
12228	Europe and Central Asia	Republic of Croatia	Financial Intermediary Loan	0	1987-03-31 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	IND.ENERGY EFFIC. I
12061	Europe and Central Asia	Republic of Croatia	Sector Investment and Maintenance Loan	0	1987-10-13 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	HIGHWAY SECTOR II
12062	Europe and Central Asia	Republic of Croatia	Sector Investment and Maintenance Loan	0	1987-10-13 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	HIGHWAY SECTOR II
12063	Europe and Central Asia	Bosnia and Herzegovina	Sector Investment and Maintenance Loan	0	1987-10-13 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	HIGHWAY SECTOR II
11696	Europe and Central Asia	Republic of Croatia	Specific Investment Loan	28000000	1989-05-23 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Istria Water Supply & Sewerage Project

	regionname	countryname	lendinginstr	totalamt	boardapprovaldate	location	GeoLocID	GeoLocName	Latitude	Longitude	Country	project_name
13966	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Financial Intermediary Loan	86000000	1980-02-26 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Agriculture Credit Project (03)
13937	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Specific Investment Loan	125000000	1980-03-25 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Highway Project (11)
13773	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Financial Intermediary Loan	110000000	1980-10-28 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Industrial Credit Project (05)
13706	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Financial Intermediary Loan	87000000	1981-03-03 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Morava Regional Development Project (02)
13647	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Specific Investment Loan	34000000	1981-04-28 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Kosovo Railway Project
13629	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Financial Intermediary Loan	90000000	1981-05-14 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Kosovo Agriculture Development Project
13555	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Financial Intermediary Loan	80000000	1981-07-14 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Macedonia Agriculture Development Project (03)
13526	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Specific Investment Loan	41000000	1981-11-10 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Kosovo Water Supply Project
13412	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Financial Intermediary Loan	66000000	1982-04-27 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Industrial Credit Project (06)
13402	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Specific Investment Loan	35000000	1982-05-04 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Bosnia Herzegovina Agriculture Development Pro...
13365	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Specific Investment Loan	34600000	1982-05-27 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Semberija Drainage Project
13231	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Specific Investment Loan	30000000	1983-01-25 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Tuzla Region Water Supply & Environment Project
13099	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Financial Intermediary Loan	136000000	1983-06-09 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Serbia Regional Development Project
13098	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Financial Intermediary Loan	79000000	1983-06-09 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Kosovo Regional Development Project
13071	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Structural Adjustment Loan	275000000	1983-06-28 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Structural Adjustment Loan Project (01)
13053	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Sector Investment and Maintenance Loan	110000000	1983-07-19 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Railway Project (06)
13046	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Financial Intermediary Loan	70000000	1983-07-26 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Industrial Credit Project (07)
13047	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Specific Investment Loan	120000000	1983-07-26 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Power Transmission Project (03) Energy Managem...
13041	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Specific Investment Loan	61000000	1983-08-02 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	MOSTAR POWER(SAP)
12809	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Financial Intermediary Loan	40000000	1984-07-31 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Montenegro Regional Development Project
12677	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Specific Investment Loan	125000000	1985-04-30 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Visegrad Hydroelectric Project
12671	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Sector Adjustment Loan	90000000	1985-05-03 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Fertilizer Sector Loan Project
12617	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Specific Investment Loan	35000000	1985-06-06 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Bosnia Herzegovina Forestry Project
12577	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Specific Investment Loan	92500000	1985-06-27 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Petroleum Sector Project
12375	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Sector Investment and Maintenance Loan	121500000	1986-06-10 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Highway Sector Project
12227	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Financial Intermediary Loan	0	1987-03-31 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	IND.ENERGY EFFIC. I
12225	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Financial Intermediary Loan	90000000	1987-03-31 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Energy Conservation & Substitution Project
12060	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Sector Investment and Maintenance Loan	68000000	1987-10-13 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Highway Sector Project (02)
11866	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Financial Intermediary Loan	120000000	1988-06-29 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Export Oriented Industries Project
11695	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Specific Investment Loan	60000000	1989-05-23 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Istria Water Supply & Sewerage Project
11694	Europe and Central Asia	Socialist Federal Republic of Yugoslavia	Sector Investment and Maintenance Loan	138000000	1989-05-23 00:00:00+00:00	NaN	NaN	NaN	NaN	NaN	NaN	Railway Project (07)

	index	Country_Name	Country_Code	Indicator_Name	Indicator_Code	1960	1961	1962	1963	1964	1965	1966	1967	1968	1969	1970	1971	1972	1973	1974	1975	1976	1977	1978	1979	1980	1981	1982	1983	1984	1985	1986	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017
0	0	Aruba	ABW	Population, total	SP.POP.TOTL	5.421100e+04	5.543800e+04	5.622500e+04	5.669500e+04	5.703200e+04	5.736000e+04	5.771500e+04	5.805500e+04	5.838600e+04	5.872600e+04	5.906300e+04	5.944000e+04	5.984000e+04	6.024300e+04	6.052800e+04	6.065700e+04	6.058600e+04	6.036600e+04	6.010300e+04	5.998000e+04	6.009600e+04	6.056700e+04	6.134500e+04	6.220100e+04	6.283600e+04	6.302600e+04	6.264400e+04	6.183300e+04	6.107900e+04	6.103200e+04	6.214900e+04	6.462200e+04	6.823500e+04	7.250400e+04	7.670000e+04	8.032400e+04	8.320000e+04	8.545100e+04	8.727700e+04	8.900500e+04	9.085300e+04	9.289800e+04	9.499200e+04	9.701700e+04	9.873700e+04	1.000310e+05	1.008320e+05	1.012200e+05	1.013530e+05	1.014530e+05	1.016690e+05	1.020530e+05	1.025770e+05	1.031870e+05	1.037950e+05	1.043410e+05	1.048220e+05	1.052640e+05
1	1	Afghanistan	AFG	Population, total	SP.POP.TOTL	8.996351e+06	9.166764e+06	9.345868e+06	9.533954e+06	9.731361e+06	9.938414e+06	1.015233e+07	1.037263e+07	1.060435e+07	1.085443e+07	1.112612e+07	1.141782e+07	1.172194e+07	1.202782e+07	1.232154e+07	1.259029e+07	1.284030e+07	1.306754e+07	1.323773e+07	1.330670e+07	1.324837e+07	1.305395e+07	1.274964e+07	1.238927e+07	1.204712e+07	1.178305e+07	1.160104e+07	1.150276e+07	1.154089e+07	1.177761e+07	1.224911e+07	1.299366e+07	1.398123e+07	1.509510e+07	1.617272e+07	1.709954e+07	1.782288e+07	1.838160e+07	1.886400e+07	1.940368e+07	2.009376e+07	2.096646e+07	2.197992e+07	2.306485e+07	2.411898e+07	2.507080e+07	2.589345e+07	2.661679e+07	2.729403e+07	2.800433e+07	2.880317e+07	2.970860e+07	3.069696e+07	3.173169e+07	3.275802e+07	3.373649e+07	3.465603e+07	3.553008e+07
2	2	Angola	AGO	Population, total	SP.POP.TOTL	5.643182e+06	5.753024e+06	5.866061e+06	5.980417e+06	6.093321e+06	6.203299e+06	6.309770e+06	6.414995e+06	6.523791e+06	6.642632e+06	6.776381e+06	6.927269e+06	7.094834e+06	7.277960e+06	7.474338e+06	7.682479e+06	7.900997e+06	8.130988e+06	8.376147e+06	8.641521e+06	8.929900e+06	9.244507e+06	9.582156e+06	9.931562e+06	1.027732e+07	1.060904e+07	1.092104e+07	1.121827e+07	1.151397e+07	1.182724e+07	1.217144e+07	1.255345e+07	1.296834e+07	1.340373e+07	1.384130e+07	1.426899e+07	1.468228e+07	1.508898e+07	1.550432e+07	1.594977e+07	1.644092e+07	1.698327e+07	1.757265e+07	1.820337e+07	1.886572e+07	1.955254e+07	2.026240e+07	2.099769e+07	2.175942e+07	2.254955e+07	2.336913e+07	2.421856e+07	2.509615e+07	2.599834e+07	2.692047e+07	2.785930e+07	2.881346e+07	2.978419e+07
3	3	Albania	ALB	Population, total	SP.POP.TOTL	1.608800e+06	1.659800e+06	1.711319e+06	1.762621e+06	1.814135e+06	1.864791e+06	1.914573e+06	1.965598e+06	2.022272e+06	2.081695e+06	2.135479e+06	2.187853e+06	2.243126e+06	2.296752e+06	2.350124e+06	2.404831e+06	2.458526e+06	2.513546e+06	2.566266e+06	2.617832e+06	2.671997e+06	2.726056e+06	2.784278e+06	2.843960e+06	2.904429e+06	2.964762e+06	3.022635e+06	3.083605e+06	3.142336e+06	3.227943e+06	3.286542e+06	3.266790e+06	3.247039e+06	3.227287e+06	3.207536e+06	3.187784e+06	3.168033e+06	3.148281e+06	3.128530e+06	3.108778e+06	3.089027e+06	3.060173e+06	3.051010e+06	3.039616e+06	3.026939e+06	3.011487e+06	2.992547e+06	2.970017e+06	2.947314e+06	2.927519e+06	2.913021e+06	2.905195e+06	2.900401e+06	2.895092e+06	2.889104e+06	2.880703e+06	2.876101e+06	2.873457e+06
4	4	Andorra	AND	Population, total	SP.POP.TOTL	1.341100e+04	1.437500e+04	1.537000e+04	1.641200e+04	1.746900e+04	1.854900e+04	1.964700e+04	2.075800e+04	2.189000e+04	2.305800e+04	2.427600e+04	2.555900e+04	2.689200e+04	2.823200e+04	2.952000e+04	3.070500e+04	3.177700e+04	3.277100e+04	3.373700e+04	3.481800e+04	3.606700e+04	3.750000e+04	3.911400e+04	4.086700e+04	4.270600e+04	4.460000e+04	4.651700e+04	4.845500e+04	5.043400e+04	5.244800e+04	5.450900e+04	5.667100e+04	5.888800e+04	6.097100e+04	6.267700e+04	6.385000e+04	6.436000e+04	6.432700e+04	6.414200e+04	6.437000e+04	6.539000e+04	6.734100e+04	7.004900e+04	7.318200e+04	7.624400e+04	7.886700e+04	8.099100e+04	8.268300e+04	8.386100e+04	8.446200e+04	8.444900e+04	8.375100e+04	8.243100e+04	8.078800e+04	7.922300e+04	7.801400e+04	7.728100e+04	7.696500e+04
5	5	Arab World	ARB	Population, total	SP.POP.TOTL	9.249093e+07	9.504450e+07	9.768229e+07	1.004111e+08	1.032399e+08	1.061750e+08	1.092306e+08	1.124069e+08	1.156802e+08	1.190165e+08	1.223984e+08	1.258074e+08	1.292694e+08	1.328634e+08	1.366968e+08	1.408433e+08	1.453324e+08	1.501331e+08	1.551837e+08	1.603925e+08	1.656895e+08	1.710520e+08	1.764901e+08	1.820058e+08	1.876108e+08	1.933103e+08	1.990938e+08	2.049425e+08	2.108448e+08	2.167874e+08	2.247354e+08	2.308299e+08	2.350372e+08	2.412861e+08	2.474359e+08	2.550297e+08	2.608435e+08	2.665751e+08	2.722351e+08	2.779629e+08	2.838320e+08	2.898504e+08	2.960266e+08	3.024345e+08	3.091620e+08	3.162647e+08	3.237733e+08	3.316538e+08	3.398255e+08	3.481451e+08	3.565089e+08	3.648959e+08	3.733070e+08	3.817021e+08	3.900430e+08	3.983050e+08	4.064527e+08	4.144919e+08
6	6	United Arab Emirates	ARE	Population, total	SP.POP.TOTL	9.263400e+04	1.010780e+05	1.124720e+05	1.255660e+05	1.385290e+05	1.503620e+05	1.604810e+05	1.702830e+05	1.831940e+05	2.038200e+05	2.354990e+05	2.788080e+05	3.327600e+05	3.971740e+05	4.713640e+05	5.543240e+05	6.469430e+05	7.481170e+05	8.522620e+05	9.520400e+05	1.042384e+06	1.120900e+06	1.189545e+06	1.253060e+06	1.318478e+06	1.391052e+06	1.472218e+06	1.560718e+06	1.655849e+06	1.756043e+06	1.860174e+06	1.970026e+06	2.086639e+06	2.207405e+06	2.328686e+06	2.448820e+06	2.571020e+06	2.700010e+06	2.838145e+06	2.988162e+06	3.154925e+06	3.326032e+06	3.507232e+06	3.741932e+06	4.087931e+06	4.579562e+06	5.242032e+06	6.044067e+06	6.894278e+06	7.666393e+06	8.270684e+06	8.672475e+06	8.900453e+06	9.006263e+06	9.070867e+06	9.154302e+06	9.269612e+06	9.400145e+06
7	7	Argentina	ARG	Population, total	SP.POP.TOTL	2.061908e+07	2.095308e+07	2.128768e+07	2.162184e+07	2.195393e+07	2.228339e+07	2.260875e+07	2.293220e+07	2.326128e+07	2.360599e+07	2.397306e+07	2.436644e+07	2.478295e+07	2.521339e+07	2.564451e+07	2.606698e+07	2.647715e+07	2.687856e+07	2.727774e+07	2.768453e+07	2.810589e+07	2.854336e+07	2.899399e+07	2.945474e+07	2.992090e+07	3.038878e+07	3.085724e+07	3.132647e+07	3.179552e+07	3.226356e+07	3.272974e+07	3.319392e+07	3.365515e+07	3.411092e+07	3.455812e+07	3.499481e+07	3.541968e+07	3.583397e+07	3.624159e+07	3.664807e+07	3.705745e+07	3.747151e+07	3.788937e+07	3.830938e+07	3.872870e+07	3.914549e+07	3.955889e+07	3.997022e+07	4.038239e+07	4.079941e+07	4.122389e+07	4.165688e+07	4.209674e+07	4.253992e+07	4.298152e+07	4.341776e+07	4.384743e+07	4.427104e+07
8	8	Armenia	ARM	Population, total	SP.POP.TOTL	1.874120e+06	1.941491e+06	2.009526e+06	2.077575e+06	2.144998e+06	2.211316e+06	2.276031e+06	2.339124e+06	2.401140e+06	2.462925e+06	2.525065e+06	2.587706e+06	2.650484e+06	2.712781e+06	2.773747e+06	2.832757e+06	2.889579e+06	2.944379e+06	2.997411e+06	3.049105e+06	3.099751e+06	3.148092e+06	3.193686e+06	3.238594e+06	3.285595e+06	3.335935e+06	3.392256e+06	3.451942e+06	3.504651e+06	3.536469e+06	3.538165e+06	3.505251e+06	3.442810e+06	3.363098e+06	3.283660e+06	3.217342e+06	3.168215e+06	3.133086e+06	3.108684e+06	3.089017e+06	3.069588e+06	3.050655e+06	3.033897e+06	3.017806e+06	3.000612e+06	2.981259e+06	2.958500e+06	2.933056e+06	2.908220e+06	2.888584e+06	2.877311e+06	2.875581e+06	2.881922e+06	2.893509e+06	2.906220e+06	2.916950e+06	2.924816e+06	2.930450e+06
9	9	American Samoa	ASM	Population, total	SP.POP.TOTL	2.001300e+04	2.048600e+04	2.111700e+04	2.188200e+04	2.269800e+04	2.352000e+04	2.432100e+04	2.511600e+04	2.588500e+04	2.661400e+04	2.729200e+04	2.791600e+04	2.849200e+04	2.901400e+04	2.948800e+04	2.993200e+04	3.032100e+04	3.068900e+04	3.110200e+04	3.167300e+04	3.245700e+04	3.349300e+04	3.473800e+04	3.616000e+04	3.768800e+04	3.924100e+04	4.083700e+04	4.245000e+04	4.404700e+04	4.559300e+04	4.703800e+04	4.837500e+04	4.959300e+04	5.072000e+04	5.180300e+04	5.286800e+04	5.392900e+04	5.494100e+04	5.590100e+04	5.677000e+04	5.752100e+04	5.817500e+04	5.873100e+04	5.911700e+04	5.926400e+04	5.911800e+04	5.865000e+04	5.790300e+04	5.703000e+04	5.622700e+04	5.563700e+04	5.532000e+04	5.523000e+04	5.530700e+04	5.543700e+04	5.553700e+04	5.559900e+04	5.564100e+04
10	10	Antigua and Barbuda	ATG	Population, total	SP.POP.TOTL	5.533900e+04	5.614400e+04	5.714400e+04	5.829400e+04	5.952400e+04	6.078100e+04	6.205900e+04	6.336000e+04	6.465500e+04	6.591000e+04	6.709800e+04	6.818800e+04	6.917600e+04	7.006600e+04	7.087800e+04	7.160900e+04	7.228500e+04	7.287500e+04	7.332400e+04	7.352800e+04	7.344200e+04	7.306600e+04	7.244800e+04	7.163900e+04	7.072500e+04	6.978200e+04	6.880900e+04	6.784500e+04	6.705800e+04	6.662700e+04	6.669600e+04	6.730700e+04	6.842700e+04	6.993800e+04	7.171900e+04	7.361900e+04	7.562800e+04	7.773900e+04	7.985100e+04	8.183100e+04	8.358400e+04	8.505700e+04	8.626600e+04	8.729300e+04	8.825700e+04	8.925300e+04	9.030100e+04	9.138100e+04	9.247800e+04	9.358100e+04	9.466100e+04	9.571900e+04	9.677700e+04	9.782400e+04	9.887500e+04	9.992300e+04	1.009630e+05	1.020120e+05
11	11	Australia	AUS	Population, total	SP.POP.TOTL	1.027648e+07	1.048300e+07	1.074200e+07	1.095000e+07	1.116700e+07	1.138800e+07	1.165100e+07	1.179900e+07	1.200900e+07	1.226300e+07	1.250700e+07	1.293700e+07	1.317700e+07	1.338000e+07	1.372300e+07	1.389300e+07	1.403300e+07	1.419200e+07	1.435800e+07	1.451400e+07	1.469200e+07	1.492700e+07	1.517800e+07	1.536900e+07	1.554400e+07	1.575800e+07	1.601840e+07	1.626390e+07	1.653220e+07	1.681440e+07	1.706510e+07	1.728400e+07	1.749500e+07	1.766700e+07	1.785500e+07	1.807200e+07	1.831100e+07	1.851700e+07	1.871100e+07	1.892600e+07	1.915300e+07	1.941300e+07	1.965140e+07	1.989540e+07	2.012740e+07	2.039480e+07	2.069790e+07	2.082760e+07	2.124920e+07	2.169170e+07	2.203175e+07	2.234002e+07	2.274248e+07	2.314590e+07	2.350414e+07	2.385078e+07	2.421081e+07	2.459893e+07
12	12	Austria	AUT	Population, total	SP.POP.TOTL	7.047539e+06	7.086299e+06	7.129864e+06	7.175811e+06	7.223801e+06	7.270889e+06	7.322066e+06	7.376998e+06	7.415403e+06	7.441055e+06	7.467086e+06	7.500482e+06	7.544201e+06	7.586115e+06	7.599038e+06	7.578903e+06	7.565525e+06	7.568430e+06	7.562305e+06	7.549425e+06	7.549433e+06	7.568710e+06	7.574140e+06	7.561910e+06	7.561434e+06	7.564985e+06	7.569794e+06	7.574586e+06	7.585317e+06	7.619567e+06	7.677850e+06	7.754891e+06	7.840709e+06	7.905633e+06	7.936118e+06	7.948278e+06	7.959017e+06	7.968041e+06	7.976789e+06	7.992324e+06	8.011566e+06	8.042293e+06	8.081957e+06	8.121423e+06	8.171966e+06	8.227829e+06	8.268641e+06	8.295487e+06	8.321496e+06	8.343323e+06	8.363404e+06	8.391643e+06	8.429991e+06	8.479823e+06	8.546356e+06	8.642699e+06	8.736668e+06	8.809212e+06
13	13	Azerbaijan	AZE	Population, total	SP.POP.TOTL	3.895396e+06	4.030320e+06	4.171425e+06	4.315128e+06	4.456689e+06	4.592610e+06	4.721525e+06	4.843870e+06	4.960235e+06	5.071930e+06	5.180025e+06	5.284532e+06	5.385267e+06	5.483084e+06	5.579077e+06	5.674137e+06	5.768724e+06	5.863134e+06	5.957929e+06	6.053645e+06	6.150738e+06	6.249320e+06	6.349558e+06	6.452076e+06	6.557585e+06	6.666455e+06	6.778633e+06	6.893500e+06	7.010036e+06	7.126891e+06	7.159000e+06	7.271000e+06	7.382000e+06	7.495000e+06	7.597000e+06	7.685000e+06	7.763000e+06	7.838250e+06	7.913000e+06	7.982750e+06	8.048600e+06	8.111200e+06	8.171950e+06	8.234100e+06	8.306500e+06	8.391850e+06	8.484550e+06	8.581300e+06	8.763400e+06	8.947243e+06	9.054332e+06	9.173082e+06	9.295784e+06	9.416801e+06	9.535079e+06	9.649341e+06	9.757812e+06	9.862429e+06
14	14	Burundi	BDI	Population, total	SP.POP.TOTL	2.786106e+06	2.839666e+06	2.893669e+06	2.949926e+06	3.010859e+06	3.077876e+06	3.152723e+06	3.234023e+06	3.316233e+06	3.391753e+06	3.455606e+06	3.505391e+06	3.544047e+06	3.578490e+06	3.618585e+06	3.671494e+06	3.739659e+06	3.821194e+06	3.913768e+06	4.013310e+06	4.116817e+06	4.223195e+06	4.333386e+06	4.448728e+06	4.571292e+06	4.702066e+06	4.841565e+06	4.987736e+06	5.135956e+06	5.280024e+06	5.415415e+06	5.542048e+06	5.661139e+06	5.771398e+06	5.871607e+06	5.962058e+06	6.041112e+06	6.112097e+06	6.186352e+06	6.278940e+06	6.400706e+06	6.555829e+06	6.741569e+06	6.953113e+06	7.182451e+06	7.423289e+06	7.675338e+06	7.939573e+06	8.212264e+06	8.489031e+06	8.766930e+06	9.043508e+06	9.319710e+06	9.600186e+06	9.891790e+06	1.019927e+07	1.052412e+07	1.086424e+07
15	15	Belgium	BEL	Population, total	SP.POP.TOTL	9.153489e+06	9.183948e+06	9.220578e+06	9.289770e+06	9.378113e+06	9.463667e+06	9.527807e+06	9.580991e+06	9.618756e+06	9.646032e+06	9.655549e+06	9.673162e+06	9.711115e+06	9.741720e+06	9.772419e+06	9.800700e+06	9.818227e+06	9.830358e+06	9.839534e+06	9.848382e+06	9.859242e+06	9.858982e+06	9.856303e+06	9.855520e+06	9.855372e+06	9.858308e+06	9.861823e+06	9.870234e+06	9.901664e+06	9.937697e+06	9.967379e+06	1.000449e+07	1.004516e+07	1.008448e+07	1.011560e+07	1.013681e+07	1.015664e+07	1.018124e+07	1.020301e+07	1.022642e+07	1.025125e+07	1.028657e+07	1.033278e+07	1.037613e+07	1.042114e+07	1.047862e+07	1.054796e+07	1.062570e+07	1.070997e+07	1.079649e+07	1.089559e+07	1.104774e+07	1.112825e+07	1.118282e+07	1.120906e+07	1.127420e+07	1.133142e+07	1.137207e+07
16	16	Benin	BEN	Population, total	SP.POP.TOTL	2.431622e+06	2.465867e+06	2.502896e+06	2.542859e+06	2.585965e+06	2.632356e+06	2.682159e+06	2.735307e+06	2.791590e+06	2.850661e+06	2.912340e+06	2.976572e+06	3.043567e+06	3.113675e+06	3.187412e+06	3.265165e+06	3.347173e+06	3.433439e+06	3.523938e+06	3.618526e+06	3.717165e+06	3.820128e+06	3.927714e+06	4.039949e+06	4.156819e+06	4.278501e+06	4.404506e+06	4.535263e+06	4.672852e+06	4.820016e+06	4.978496e+06	5.149499e+06	5.331803e+06	5.521763e+06	5.714220e+06	5.905558e+06	6.094259e+06	6.281639e+06	6.470265e+06	6.664098e+06	6.865951e+06	7.076733e+06	7.295394e+06	7.520555e+06	7.750004e+06	7.982225e+06	8.216896e+06	8.454791e+06	8.696916e+06	8.944706e+06	9.199259e+06	9.460802e+06	9.729160e+06	1.000445e+07	1.028671e+07	1.057595e+07	1.087230e+07	1.117569e+07
17	17	Burkina Faso	BFA	Population, total	SP.POP.TOTL	4.829288e+06	4.894580e+06	4.960326e+06	5.027821e+06	5.098890e+06	5.174870e+06	5.256363e+06	5.343019e+06	5.434041e+06	5.528174e+06	5.624600e+06	5.723381e+06	5.825173e+06	5.930483e+06	6.040041e+06	6.154545e+06	6.274037e+06	6.398935e+06	6.530819e+06	6.671656e+06	6.822843e+06	6.985160e+06	7.158255e+06	7.340905e+06	7.531242e+06	7.727907e+06	7.930694e+06	8.140073e+06	8.356305e+06	8.579823e+06	8.811034e+06	9.050084e+06	9.297113e+06	9.552476e+06	9.816588e+06	1.008988e+07	1.037274e+07	1.066555e+07	1.096872e+07	1.128270e+07	1.160794e+07	1.194459e+07	1.229310e+07	1.265462e+07	1.303057e+07	1.342193e+07	1.382918e+07	1.425202e+07	1.468973e+07	1.514110e+07	1.560522e+07	1.608190e+07	1.657122e+07	1.707272e+07	1.758598e+07	1.811062e+07	1.864643e+07	1.919338e+07
18	18	Bangladesh	BGD	Population, total	SP.POP.TOTL	4.819975e+07	4.959280e+07	5.103014e+07	5.253242e+07	5.412910e+07	5.583404e+07	5.767299e+07	5.962067e+07	6.157947e+07	6.341739e+07	6.504777e+07	6.642474e+07	6.759747e+07	6.869118e+07	6.988442e+07	7.130592e+07	7.299914e+07	7.492590e+07	7.703385e+07	7.923678e+07	8.147086e+07	8.372127e+07	8.600733e+07	8.833824e+07	9.073236e+07	9.319986e+07	9.574243e+07	9.834381e+07	1.009753e+08	1.035992e+08	1.061886e+08	1.087274e+08	1.112219e+08	1.136951e+08	1.161823e+08	1.187069e+08	1.212696e+08	1.238546e+08	1.264480e+08	1.290297e+08	1.315812e+08	1.341072e+08	1.366007e+08	1.390190e+08	1.413075e+08	1.434311e+08	1.453680e+08	1.471392e+08	1.488058e+08	1.504547e+08	1.521491e+08	1.539119e+08	1.557271e+08	1.575713e+08	1.594053e+08	1.612009e+08	1.629516e+08	1.646698e+08
19	19	Bulgaria	BGR	Population, total	SP.POP.TOTL	7.867374e+06	7.943118e+06	8.012946e+06	8.078145e+06	8.144340e+06	8.204168e+06	8.258057e+06	8.310226e+06	8.369603e+06	8.434172e+06	8.489574e+06	8.536395e+06	8.576200e+06	8.620967e+06	8.678745e+06	8.720742e+06	8.758599e+06	8.804183e+06	8.814032e+06	8.825940e+06	8.861535e+06	8.891117e+06	8.917457e+06	8.939738e+06	8.960679e+06	8.960547e+06	8.958171e+06	8.971359e+06	8.981446e+06	8.876972e+06	8.718289e+06	8.632367e+06	8.540164e+06	8.472313e+06	8.443591e+06	8.406067e+06	8.362826e+06	8.312068e+06	8.256786e+06	8.210624e+06	8.170172e+06	8.009142e+06	7.837161e+06	7.775327e+06	7.716860e+06	7.658972e+06	7.601022e+06	7.545338e+06	7.492561e+06	7.444443e+06	7.395599e+06	7.348328e+06	7.305888e+06	7.265115e+06	7.223938e+06	7.177991e+06	7.127822e+06	7.075991e+06
20	20	Bahrain	BHR	Population, total	SP.POP.TOTL	1.624270e+05	1.678940e+05	1.731440e+05	1.781400e+05	1.828870e+05	1.874310e+05	1.917800e+05	1.960630e+05	2.006530e+05	2.060430e+05	2.126050e+05	2.203120e+05	2.291550e+05	2.395270e+05	2.519110e+05	2.665430e+05	2.837520e+05	3.031750e+05	3.234730e+05	3.427980e+05	3.598880e+05	3.741200e+05	3.859500e+05	3.964540e+05	4.072270e+05	4.194300e+05	4.334820e+05	4.489730e+05	4.652020e+05	4.810900e+05	4.959310e+05	5.097650e+05	5.230870e+05	5.362130e+05	5.495880e+05	5.636990e+05	5.786680e+05	5.949300e+05	6.137020e+05	6.365450e+05	6.646140e+05	6.975490e+05	7.351480e+05	7.787110e+05	8.298480e+05	8.891680e+05	9.584140e+05	1.035891e+06	1.114590e+06	1.185029e+06	1.240862e+06	1.278269e+06	1.300217e+06	1.315411e+06	1.336397e+06	1.371855e+06	1.425171e+06	1.492584e+06
21	21	Bahamas, The	BHS	Population, total	SP.POP.TOTL	1.095280e+05	1.151080e+05	1.210830e+05	1.273330e+05	1.336980e+05	1.400540e+05	1.463660e+05	1.526090e+05	1.586270e+05	1.642480e+05	1.693540e+05	1.738630e+05	1.778390e+05	1.814880e+05	1.850990e+05	1.888820e+05	1.929020e+05	1.971110e+05	2.015130e+05	2.060320e+05	2.106610e+05	2.153960e+05	2.202750e+05	2.251870e+05	2.300150e+05	2.346870e+05	2.391310e+05	2.433930e+05	2.475790e+05	2.518490e+05	2.563360e+05	2.611160e+05	2.661340e+05	2.711650e+05	2.758950e+05	2.801500e+05	2.837900e+05	2.869700e+05	2.900600e+05	2.935720e+05	2.978900e+05	3.031350e+05	3.091570e+05	3.157460e+05	3.225260e+05	3.292490e+05	3.358300e+05	3.423280e+05	3.486760e+05	3.548560e+05	3.608320e+05	3.665680e+05	3.720390e+05	3.772400e+05	3.821690e+05	3.868380e+05	3.912320e+05	3.953610e+05
22	22	Bosnia and Herzegovina	BIH	Population, total	SP.POP.TOTL	3.225668e+06	3.288602e+06	3.353226e+06	3.417574e+06	3.478995e+06	3.535640e+06	3.586634e+06	3.632669e+06	3.675452e+06	3.717466e+06	3.760527e+06	3.805285e+06	3.851151e+06	3.897255e+06	3.942223e+06	3.985103e+06	4.025265e+06	4.063191e+06	4.100350e+06	4.138819e+06	4.179855e+06	4.222511e+06	4.265310e+06	4.308106e+06	4.350746e+06	4.392130e+06	4.435504e+06	4.478519e+06	4.508056e+06	4.506653e+06	4.463422e+06	4.371603e+06	4.239154e+06	4.087999e+06	3.948816e+06	3.843712e+06	3.780378e+06	3.752431e+06	3.750485e+06	3.759118e+06	3.766706e+06	3.771284e+06	3.775807e+06	3.779247e+06	3.781287e+06	3.781530e+06	3.779468e+06	3.774000e+06	3.763599e+06	3.746561e+06	3.722084e+06	3.688865e+06	3.648200e+06	3.604999e+06	3.566002e+06	3.535961e+06	3.516816e+06	3.507017e+06
23	23	Belarus	BLR	Population, total	SP.POP.TOTL	8.198000e+06	8.271216e+06	8.351928e+06	8.437232e+06	8.524224e+06	8.610000e+06	8.696496e+06	8.785648e+06	8.874552e+06	8.960304e+06	9.040000e+06	9.115576e+06	9.188968e+06	9.257272e+06	9.317584e+06	9.367000e+06	9.411000e+06	9.463000e+06	9.525000e+06	9.584000e+06	9.643000e+06	9.710000e+06	9.776000e+06	9.843000e+06	9.910000e+06	9.975000e+06	1.004300e+07	1.011100e+07	1.014000e+07	1.017000e+07	1.018900e+07	1.019400e+07	1.021600e+07	1.023900e+07	1.022700e+07	1.019400e+07	1.016000e+07	1.011700e+07	1.006900e+07	1.002674e+07	9.979610e+06	9.928549e+06	9.865548e+06	9.796749e+06	9.730146e+06	9.663915e+06	9.604924e+06	9.560953e+06	9.527985e+06	9.506765e+06	9.490583e+06	9.473172e+06	9.464495e+06	9.465997e+06	9.474511e+06	9.489616e+06	9.501534e+06	9.507875e+06
24	24	Belize	BLZ	Population, total	SP.POP.TOTL	9.206400e+04	9.470300e+04	9.738400e+04	1.001640e+05	1.030690e+05	1.061190e+05	1.093470e+05	1.126920e+05	1.160610e+05	1.192610e+05	1.221820e+05	1.247930e+05	1.271500e+05	1.292940e+05	1.313070e+05	1.332600e+05	1.351470e+05	1.369890e+05	1.389650e+05	1.413050e+05	1.441550e+05	1.475660e+05	1.515000e+05	1.558220e+05	1.603470e+05	1.649210e+05	1.695680e+05	1.743200e+05	1.790280e+05	1.834690e+05	1.875520e+05	1.911260e+05	1.943170e+05	1.976160e+05	2.016740e+05	2.069630e+05	2.136760e+05	2.216060e+05	2.302840e+05	2.390260e+05	2.473150e+05	2.549840e+05	2.622060e+05	2.691300e+05	2.760890e+05	2.832770e+05	2.907470e+05	2.984070e+05	3.061650e+05	3.139290e+05	3.216080e+05	3.291920e+05	3.367010e+05	3.441810e+05	3.516940e+05	3.592880e+05	3.669540e+05	3.746810e+05
25	25	Bermuda	BMU	Population, total	SP.POP.TOTL	4.440000e+04	4.550000e+04	4.660000e+04	4.770000e+04	4.890000e+04	5.010000e+04	5.100000e+04	5.200000e+04	5.300000e+04	5.400000e+04	5.500000e+04	5.460000e+04	5.420000e+04	5.380000e+04	5.340000e+04	5.300000e+04	5.320000e+04	5.340000e+04	5.360000e+04	5.380000e+04	5.467000e+04	5.505000e+04	5.544900e+04	5.593000e+04	5.642300e+04	5.689800e+04	5.738200e+04	5.784900e+04	5.834700e+04	5.884100e+04	5.932600e+04	5.902100e+04	5.859500e+04	5.891000e+04	5.932000e+04	5.974600e+04	6.012900e+04	6.049700e+04	6.094300e+04	6.128500e+04	6.183300e+04	6.250400e+04	6.291200e+04	6.332500e+04	6.374000e+04	6.415400e+04	6.452300e+04	6.488800e+04	6.527300e+04	6.563600e+04	6.512400e+04	6.456400e+04	6.479800e+04	6.500100e+04	6.513900e+04	6.523900e+04	6.534100e+04	6.544100e+04
26	26	Bolivia	BOL	Population, total	SP.POP.TOTL	3.693449e+06	3.764813e+06	3.838097e+06	3.913395e+06	3.990857e+06	4.070590e+06	4.152668e+06	4.237125e+06	4.324064e+06	4.413590e+06	4.505778e+06	4.600591e+06	4.698083e+06	4.798509e+06	4.902168e+06	5.009257e+06	5.119833e+06	5.233677e+06	5.350322e+06	5.469123e+06	5.589575e+06	5.711599e+06	5.835182e+06	5.959960e+06	6.085496e+06	6.211550e+06	6.337893e+06	6.464732e+06	6.592787e+06	6.723046e+06	6.856244e+06	6.992521e+06	7.131707e+06	7.273825e+06	7.418861e+06	7.566714e+06	7.717443e+06	7.870855e+06	8.026254e+06	8.182712e+06	8.339512e+06	8.496375e+06	8.653345e+06	8.810420e+06	8.967741e+06	9.125409e+06	9.283334e+06	9.441444e+06	9.599855e+06	9.758748e+06	9.918242e+06	1.007834e+07	1.023900e+07	1.040026e+07	1.056216e+07	1.072470e+07	1.088788e+07	1.105160e+07
27	27	Brazil	BRA	Population, total	SP.POP.TOTL	7.220755e+07	7.435176e+07	7.657325e+07	7.885402e+07	8.116865e+07	8.349802e+07	8.583780e+07	8.819138e+07	9.055706e+07	9.293507e+07	9.532679e+07	9.772896e+07	1.001436e+08	1.025843e+08	1.050694e+08	1.076121e+08	1.102131e+08	1.128679e+08	1.155777e+08	1.183426e+08	1.211598e+08	1.240309e+08	1.269474e+08	1.298823e+08	1.328007e+08	1.356763e+08	1.384995e+08	1.412735e+08	1.440015e+08	1.466920e+08	1.493521e+08	1.519766e+08	1.545643e+08	1.571327e+08	1.597051e+08	1.622966e+08	1.649133e+08	1.675452e+08	1.701706e+08	1.727592e+08	1.752876e+08	1.777507e+08	1.801510e+08	1.824821e+08	1.847385e+08	1.869174e+08	1.890124e+08	1.910266e+08	1.929790e+08	1.948960e+08	1.967963e+08	1.986867e+08	2.005610e+08	2.024086e+08	2.042131e+08	2.059621e+08	2.076529e+08	2.092883e+08
28	28	Barbados	BRB	Population, total	SP.POP.TOTL	2.309390e+05	2.316780e+05	2.325860e+05	2.335870e+05	2.345470e+05	2.353740e+05	2.360440e+05	2.366210e+05	2.371990e+05	2.379130e+05	2.388480e+05	2.400350e+05	2.414410e+05	2.429760e+05	2.445390e+05	2.460340e+05	2.474440e+05	2.487840e+05	2.500320e+05	2.511770e+05	2.521940e+05	2.530800e+05	2.538410e+05	2.545180e+05	2.551930e+05	2.559240e+05	2.567360e+05	2.576110e+05	2.585270e+05	2.594580e+05	2.603740e+05	2.612750e+05	2.621840e+05	2.630890e+05	2.640150e+05	2.649590e+05	2.659420e+05	2.669450e+05	2.679500e+05	2.689220e+05	2.698470e+05	2.706850e+05	2.714780e+05	2.722580e+05	2.730910e+05	2.740090e+05	2.750390e+05	2.761500e+05	2.773190e+05	2.784700e+05	2.795690e+05	2.806010e+05	2.815850e+05	2.825090e+05	2.833850e+05	2.842170e+05	2.849960e+05	2.857190e+05
29	29	Brunei Darussalam	BRN	Population, total	SP.POP.TOTL	8.174500e+04	8.559600e+04	8.951600e+04	9.357600e+04	9.784800e+04	1.024250e+05	1.073160e+05	1.124940e+05	1.179500e+05	1.236530e+05	1.295830e+05	1.357260e+05	1.420730e+05	1.485600e+05	1.551090e+05	1.616710e+05	1.682240e+05	1.747730e+05	1.812570e+05	1.876560e+05	1.939490e+05	2.000850e+05	2.061280e+05	2.121360e+05	2.182270e+05	2.245120e+05	2.309720e+05	2.376220e+05	2.444580e+05	2.515140e+05	2.587850e+05	2.662740e+05	2.739630e+05	2.817510e+05	2.895250e+05	2.971920e+05	3.046990e+05	3.120380e+05	3.192220e+05	3.262890e+05	3.332410e+05	3.401170e+05	3.468670e+05	3.533890e+05	3.595230e+05	3.651580e+05	3.702500e+05	3.748640e+05	3.792520e+05	3.837720e+05	3.886620e+05	3.940130e+05	3.997480e+05	4.057160e+05	4.117040e+05	4.175420e+05	4.231960e+05	4.286970e+05
30	30	Bhutan	BTN	Population, total	SP.POP.TOTL	2.232880e+05	2.289180e+05	2.347060e+05	2.407780e+05	2.473250e+05	2.544640e+05	2.622440e+05	2.706220e+05	2.795150e+05	2.887740e+05	2.983010e+05	3.080530e+05	3.180450e+05	3.283120e+05	3.389430e+05	3.499820e+05	3.614550e+05	3.733240e+05	3.853840e+05	3.973900e+05	4.091720e+05	4.203800e+05	4.310500e+05	4.418470e+05	4.537200e+05	4.671780e+05	4.829520e+05	5.004370e+05	5.172730e+05	5.302570e+05	5.372800e+05	5.372840e+05	5.315250e+05	5.231170e+05	5.165030e+05	5.148770e+05	5.192820e+05	5.287540e+05	5.421550e+05	5.575430e+05	5.734160e+05	5.896000e+05	6.063990e+05	6.234340e+05	6.402820e+05	6.566390e+05	6.722280e+05	6.869580e+05	7.009500e+05	7.144580e+05	7.276410e+05	7.405100e+05	7.529670e+05	7.649610e+05	7.764480e+05	7.873860e+05	7.977650e+05	8.076100e+05
31	31	Botswana	BWA	Population, total	SP.POP.TOTL	5.245520e+05	5.372490e+05	5.508400e+05	5.653530e+05	5.807990e+05	5.971900e+05	6.146130e+05	6.331540e+05	6.528430e+05	6.736400e+05	6.955970e+05	7.186390e+05	7.428350e+05	7.685120e+05	7.960950e+05	8.258400e+05	8.578550e+05	8.919260e+05	9.275850e+05	9.641660e+05	1.001158e+06	1.038397e+06	1.075889e+06	1.113539e+06	1.151292e+06	1.189114e+06	1.226810e+06	1.264314e+06	1.301818e+06	1.339624e+06	1.377912e+06	1.416731e+06	1.455833e+06	1.494693e+06	1.532622e+06	1.569094e+06	1.604060e+06	1.637635e+06	1.669625e+06	1.699862e+06	1.728340e+06	1.754935e+06	1.779953e+06	1.804339e+06	1.829330e+06	1.855852e+06	1.884238e+06	1.914414e+06	1.946351e+06	1.979882e+06	2.014866e+06	2.051339e+06	2.089315e+06	2.128507e+06	2.168573e+06	2.209197e+06	2.250260e+06	2.291661e+06
32	32	Central African Republic	CAF	Population, total	SP.POP.TOTL	1.503508e+06	1.529227e+06	1.556661e+06	1.585763e+06	1.616516e+06	1.648833e+06	1.682885e+06	1.718603e+06	1.755344e+06	1.792220e+06	1.828709e+06	1.864598e+06	1.900317e+06	1.936841e+06	1.975521e+06	2.017372e+06	2.062405e+06	2.110457e+06	2.162249e+06	2.218575e+06	2.279821e+06	2.346797e+06	2.418844e+06	2.493135e+06	2.565803e+06	2.634232e+06	2.696982e+06	2.755244e+06	2.812244e+06	2.872668e+06	2.939780e+06	3.014624e+06	3.095807e+06	3.181222e+06	3.267670e+06	3.352767e+06	3.435821e+06	3.517309e+06	3.597385e+06	3.676508e+06	3.754986e+06	3.832203e+06	3.907612e+06	3.981665e+06	4.055036e+06	4.127910e+06	4.201758e+06	4.275800e+06	4.345386e+06	4.404230e+06	4.448525e+06	4.476153e+06	4.490416e+06	4.499653e+06	4.515392e+06	4.546100e+06	4.594621e+06	4.659080e+06
33	33	Canada	CAN	Population, total	SP.POP.TOTL	1.790901e+07	1.827100e+07	1.861400e+07	1.896400e+07	1.932500e+07	1.967800e+07	2.004800e+07	2.041200e+07	2.074400e+07	2.102800e+07	2.132400e+07	2.164554e+07	2.199363e+07	2.236941e+07	2.277409e+07	2.320900e+07	2.351800e+07	2.379600e+07	2.403600e+07	2.427700e+07	2.459300e+07	2.490000e+07	2.520200e+07	2.545600e+07	2.570200e+07	2.594200e+07	2.620400e+07	2.655000e+07	2.689500e+07	2.737900e+07	2.779100e+07	2.817168e+07	2.851960e+07	2.883341e+07	2.911191e+07	2.935400e+07	2.967190e+07	2.998720e+07	3.024790e+07	3.049920e+07	3.076970e+07	3.108190e+07	3.136200e+07	3.167600e+07	3.199500e+07	3.231200e+07	3.257050e+07	3.288793e+07	3.324577e+07	3.362857e+07	3.400527e+07	3.434278e+07	3.475054e+07	3.515237e+07	3.553535e+07	3.583251e+07	3.626460e+07	3.670808e+07
34	34	Central Europe and the Baltics	CEB	Population, total	SP.POP.TOTL	9.140158e+07	9.223712e+07	9.301489e+07	9.384575e+07	9.472260e+07	9.544706e+07	9.614864e+07	9.704359e+07	9.788239e+07	9.860214e+07	9.913330e+07	9.963898e+07	1.003636e+08	1.011205e+08	1.019463e+08	1.028625e+08	1.037701e+08	1.045893e+08	1.053043e+08	1.059248e+08	1.065649e+08	1.071880e+08	1.077708e+08	1.083269e+08	1.088532e+08	1.093603e+08	1.098471e+08	1.102967e+08	1.106885e+08	1.108014e+08	1.107458e+08	1.102904e+08	1.100056e+08	1.100815e+08	1.100196e+08	1.099132e+08	1.095631e+08	1.094591e+08	1.092072e+08	1.091024e+08	1.084055e+08	1.078004e+08	1.070976e+08	1.067608e+08	1.064661e+08	1.061738e+08	1.059013e+08	1.055045e+08	1.051267e+08	1.049244e+08	1.045438e+08	1.041740e+08	1.039353e+08	1.037137e+08	1.034962e+08	1.032578e+08	1.029943e+08	1.027271e+08
35	35	Switzerland	CHE	Population, total	SP.POP.TOTL	5.327827e+06	5.434294e+06	5.573815e+06	5.694247e+06	5.789228e+06	5.856472e+06	5.918002e+06	5.991785e+06	6.067714e+06	6.136387e+06	6.180877e+06	6.213399e+06	6.260956e+06	6.307347e+06	6.341405e+06	6.338632e+06	6.302504e+06	6.281174e+06	6.281738e+06	6.294365e+06	6.319408e+06	6.354074e+06	6.391309e+06	6.418773e+06	6.441865e+06	6.470365e+06	6.504124e+06	6.545106e+06	6.593386e+06	6.646912e+06	6.715519e+06	6.799978e+06	6.875364e+06	6.938265e+06	6.993795e+06	7.040687e+06	7.071850e+06	7.088906e+06	7.110001e+06	7.143991e+06	7.184250e+06	7.229854e+06	7.284753e+06	7.339001e+06	7.389625e+06	7.437115e+06	7.483934e+06	7.551117e+06	7.647675e+06	7.743831e+06	7.824909e+06	7.912398e+06	7.996861e+06	8.089346e+06	8.188649e+06	8.282396e+06	8.373338e+06	8.466017e+06
36	36	Channel Islands	CHI	Population, total	SP.POP.TOTL	1.094200e+05	1.103990e+05	1.114570e+05	1.125950e+05	1.137730e+05	1.149950e+05	1.162270e+05	1.174740e+05	1.187260e+05	1.199720e+05	1.211970e+05	1.224130e+05	1.236140e+05	1.247250e+05	1.256820e+05	1.264150e+05	1.269020e+05	1.271830e+05	1.273900e+05	1.276920e+05	1.282120e+05	1.289810e+05	1.299790e+05	1.311560e+05	1.324530e+05	1.338080e+05	1.352300e+05	1.367160e+05	1.381870e+05	1.395300e+05	1.406710e+05	1.415680e+05	1.422580e+05	1.428190e+05	1.433840e+05	1.440460e+05	1.448290e+05	1.457150e+05	1.466710e+05	1.476870e+05	1.487250e+05	1.497930e+05	1.509010e+05	1.520380e+05	1.531700e+05	1.542940e+05	1.554110e+05	1.565130e+05	1.575810e+05	1.586030e+05	1.595810e+05	1.604970e+05	1.613580e+05	1.621800e+05	1.629690e+05	1.637580e+05	1.645410e+05	1.653140e+05
37	37	Chile	CHL	Population, total	SP.POP.TOTL	7.716625e+06	7.890156e+06	8.067136e+06	8.247415e+06	8.430838e+06	8.617077e+06	8.806137e+06	8.997325e+06	9.188822e+06	9.378243e+06	9.563865e+06	9.745189e+06	9.922558e+06	1.009630e+07	1.026706e+07	1.043553e+07	1.060184e+07	1.076642e+07	1.093078e+07	1.109687e+07	1.126623e+07	1.143914e+07	1.161584e+07	1.179753e+07	1.198566e+07	1.218103e+07	1.238411e+07	1.259414e+07	1.280902e+07	1.302580e+07	1.324213e+07	1.345724e+07	1.367103e+07	1.388267e+07	1.409139e+07	1.429661e+07	1.449783e+07	1.469484e+07	1.488776e+07	1.507695e+07	1.526275e+07	1.544497e+07	1.562364e+07	1.579954e+07	1.597378e+07	1.614706e+07	1.631979e+07	1.649169e+07	1.666194e+07	1.682944e+07	1.699335e+07	1.715336e+07	1.730975e+07	1.746298e+07	1.761380e+07	1.776268e+07	1.790975e+07	1.805473e+07
38	38	China	CHN	Population, total	SP.POP.TOTL	6.670700e+08	6.603300e+08	6.657700e+08	6.823350e+08	6.983550e+08	7.151850e+08	7.354000e+08	7.545500e+08	7.745100e+08	7.960250e+08	8.183150e+08	8.411050e+08	8.620300e+08	8.819400e+08	9.003500e+08	9.163950e+08	9.306850e+08	9.434550e+08	9.561650e+08	9.690050e+08	9.812350e+08	9.938850e+08	1.008630e+09	1.023310e+09	1.036825e+09	1.051040e+09	1.066790e+09	1.084035e+09	1.101630e+09	1.118650e+09	1.135185e+09	1.150780e+09	1.164970e+09	1.178440e+09	1.191835e+09	1.204855e+09	1.217550e+09	1.230075e+09	1.241935e+09	1.252735e+09	1.262645e+09	1.271850e+09	1.280400e+09	1.288400e+09	1.296075e+09	1.303720e+09	1.311020e+09	1.317885e+09	1.324655e+09	1.331260e+09	1.337705e+09	1.344130e+09	1.350695e+09	1.357380e+09	1.364270e+09	1.371220e+09	1.378665e+09	1.386395e+09
39	39	Cote d'Ivoire	CIV	Population, total	SP.POP.TOTL	3.558988e+06	3.694205e+06	3.841071e+06	3.996941e+06	4.157965e+06	4.321791e+06	4.487204e+06	4.656353e+06	4.834279e+06	5.027971e+06	5.242395e+06	5.479338e+06	5.737281e+06	6.013862e+06	6.305287e+06	6.608609e+06	6.922982e+06	7.248828e+06	7.585914e+06	7.934279e+06	8.293675e+06	8.664057e+06	9.044473e+06	9.432731e+06	9.826055e+06	1.022256e+07	1.062027e+07	1.101965e+07	1.142426e+07	1.183924e+07	1.226775e+07	1.271001e+07	1.316302e+07	1.362273e+07	1.408361e+07	1.454082e+07	1.499525e+07	1.544599e+07	1.588455e+07	1.630023e+07	1.668656e+07	1.704015e+07	1.736652e+07	1.767936e+07	1.799774e+07	1.833630e+07	1.869944e+07	1.908594e+07	1.949799e+07	1.993637e+07	2.040133e+07	2.089531e+07	2.141860e+07	2.196631e+07	2.253135e+07	2.310847e+07	2.369592e+07	2.429475e+07

	index	Country_Name	Country_Code	Indicator_Name	Indicator_Code	1960	1961	1962	1963	1964	1965	1966	1967	1968	1969	1970	1971	1972	1973	1974	1975	1976	1977	1978	1979	1980	1981	1982	1983	1984	1985	1986	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017
0	0	Aruba	ABW	Population, total	SP.POP.TOTL	5.421100e+04	5.543800e+04	5.622500e+04	5.669500e+04	5.703200e+04	5.736000e+04	5.771500e+04	5.805500e+04	5.838600e+04	5.872600e+04	5.906300e+04	5.944000e+04	5.984000e+04	6.024300e+04	6.052800e+04	6.065700e+04	6.058600e+04	6.036600e+04	6.010300e+04	5.998000e+04	6.009600e+04	6.056700e+04	6.134500e+04	6.220100e+04	6.283600e+04	6.302600e+04	6.264400e+04	6.183300e+04	6.107900e+04	6.103200e+04	6.214900e+04	6.462200e+04	6.823500e+04	7.250400e+04	7.670000e+04	8.032400e+04	8.320000e+04	8.545100e+04	8.727700e+04	8.900500e+04	9.085300e+04	9.289800e+04	9.499200e+04	9.701700e+04	9.873700e+04	1.000310e+05	1.008320e+05	1.012200e+05	1.013530e+05	1.014530e+05	1.016690e+05	1.020530e+05	1.025770e+05	1.031870e+05	1.037950e+05	1.043410e+05	1.048220e+05	1.052640e+05
1	1	Afghanistan	AFG	Population, total	SP.POP.TOTL	8.996351e+06	9.166764e+06	9.345868e+06	9.533954e+06	9.731361e+06	9.938414e+06	1.015233e+07	1.037263e+07	1.060435e+07	1.085443e+07	1.112612e+07	1.141782e+07	1.172194e+07	1.202782e+07	1.232154e+07	1.259029e+07	1.284030e+07	1.306754e+07	1.323773e+07	1.330670e+07	1.324837e+07	1.305395e+07	1.274964e+07	1.238927e+07	1.204712e+07	1.178305e+07	1.160104e+07	1.150276e+07	1.154089e+07	1.177761e+07	1.224911e+07	1.299366e+07	1.398123e+07	1.509510e+07	1.617272e+07	1.709954e+07	1.782288e+07	1.838160e+07	1.886400e+07	1.940368e+07	2.009376e+07	2.096646e+07	2.197992e+07	2.306485e+07	2.411898e+07	2.507080e+07	2.589345e+07	2.661679e+07	2.729403e+07	2.800433e+07	2.880317e+07	2.970860e+07	3.069696e+07	3.173169e+07	3.275802e+07	3.373649e+07	3.465603e+07	3.553008e+07
2	2	Angola	AGO	Population, total	SP.POP.TOTL	5.643182e+06	5.753024e+06	5.866061e+06	5.980417e+06	6.093321e+06	6.203299e+06	6.309770e+06	6.414995e+06	6.523791e+06	6.642632e+06	6.776381e+06	6.927269e+06	7.094834e+06	7.277960e+06	7.474338e+06	7.682479e+06	7.900997e+06	8.130988e+06	8.376147e+06	8.641521e+06	8.929900e+06	9.244507e+06	9.582156e+06	9.931562e+06	1.027732e+07	1.060904e+07	1.092104e+07	1.121827e+07	1.151397e+07	1.182724e+07	1.217144e+07	1.255345e+07	1.296834e+07	1.340373e+07	1.384130e+07	1.426899e+07	1.468228e+07	1.508898e+07	1.550432e+07	1.594977e+07	1.644092e+07	1.698327e+07	1.757265e+07	1.820337e+07	1.886572e+07	1.955254e+07	2.026240e+07	2.099769e+07	2.175942e+07	2.254955e+07	2.336913e+07	2.421856e+07	2.509615e+07	2.599834e+07	2.692047e+07	2.785930e+07	2.881346e+07	2.978419e+07
3	3	Albania	ALB	Population, total	SP.POP.TOTL	1.608800e+06	1.659800e+06	1.711319e+06	1.762621e+06	1.814135e+06	1.864791e+06	1.914573e+06	1.965598e+06	2.022272e+06	2.081695e+06	2.135479e+06	2.187853e+06	2.243126e+06	2.296752e+06	2.350124e+06	2.404831e+06	2.458526e+06	2.513546e+06	2.566266e+06	2.617832e+06	2.671997e+06	2.726056e+06	2.784278e+06	2.843960e+06	2.904429e+06	2.964762e+06	3.022635e+06	3.083605e+06	3.142336e+06	3.227943e+06	3.286542e+06	3.266790e+06	3.247039e+06	3.227287e+06	3.207536e+06	3.187784e+06	3.168033e+06	3.148281e+06	3.128530e+06	3.108778e+06	3.089027e+06	3.060173e+06	3.051010e+06	3.039616e+06	3.026939e+06	3.011487e+06	2.992547e+06	2.970017e+06	2.947314e+06	2.927519e+06	2.913021e+06	2.905195e+06	2.900401e+06	2.895092e+06	2.889104e+06	2.880703e+06	2.876101e+06	2.873457e+06
4	4	Andorra	AND	Population, total	SP.POP.TOTL	1.341100e+04	1.437500e+04	1.537000e+04	1.641200e+04	1.746900e+04	1.854900e+04	1.964700e+04	2.075800e+04	2.189000e+04	2.305800e+04	2.427600e+04	2.555900e+04	2.689200e+04	2.823200e+04	2.952000e+04	3.070500e+04	3.177700e+04	3.277100e+04	3.373700e+04	3.481800e+04	3.606700e+04	3.750000e+04	3.911400e+04	4.086700e+04	4.270600e+04	4.460000e+04	4.651700e+04	4.845500e+04	5.043400e+04	5.244800e+04	5.450900e+04	5.667100e+04	5.888800e+04	6.097100e+04	6.267700e+04	6.385000e+04	6.436000e+04	6.432700e+04	6.414200e+04	6.437000e+04	6.539000e+04	6.734100e+04	7.004900e+04	7.318200e+04	7.624400e+04	7.886700e+04	8.099100e+04	8.268300e+04	8.386100e+04	8.446200e+04	8.444900e+04	8.375100e+04	8.243100e+04	8.078800e+04	7.922300e+04	7.801400e+04	7.728100e+04	7.696500e+04
5	5	Arab World	ARB	Population, total	SP.POP.TOTL	9.249093e+07	9.504450e+07	9.768229e+07	1.004111e+08	1.032399e+08	1.061750e+08	1.092306e+08	1.124069e+08	1.156802e+08	1.190165e+08	1.223984e+08	1.258074e+08	1.292694e+08	1.328634e+08	1.366968e+08	1.408433e+08	1.453324e+08	1.501331e+08	1.551837e+08	1.603925e+08	1.656895e+08	1.710520e+08	1.764901e+08	1.820058e+08	1.876108e+08	1.933103e+08	1.990938e+08	2.049425e+08	2.108448e+08	2.167874e+08	2.247354e+08	2.308299e+08	2.350372e+08	2.412861e+08	2.474359e+08	2.550297e+08	2.608435e+08	2.665751e+08	2.722351e+08	2.779629e+08	2.838320e+08	2.898504e+08	2.960266e+08	3.024345e+08	3.091620e+08	3.162647e+08	3.237733e+08	3.316538e+08	3.398255e+08	3.481451e+08	3.565089e+08	3.648959e+08	3.733070e+08	3.817021e+08	3.900430e+08	3.983050e+08	4.064527e+08	4.144919e+08
6	6	United Arab Emirates	ARE	Population, total	SP.POP.TOTL	9.263400e+04	1.010780e+05	1.124720e+05	1.255660e+05	1.385290e+05	1.503620e+05	1.604810e+05	1.702830e+05	1.831940e+05	2.038200e+05	2.354990e+05	2.788080e+05	3.327600e+05	3.971740e+05	4.713640e+05	5.543240e+05	6.469430e+05	7.481170e+05	8.522620e+05	9.520400e+05	1.042384e+06	1.120900e+06	1.189545e+06	1.253060e+06	1.318478e+06	1.391052e+06	1.472218e+06	1.560718e+06	1.655849e+06	1.756043e+06	1.860174e+06	1.970026e+06	2.086639e+06	2.207405e+06	2.328686e+06	2.448820e+06	2.571020e+06	2.700010e+06	2.838145e+06	2.988162e+06	3.154925e+06	3.326032e+06	3.507232e+06	3.741932e+06	4.087931e+06	4.579562e+06	5.242032e+06	6.044067e+06	6.894278e+06	7.666393e+06	8.270684e+06	8.672475e+06	8.900453e+06	9.006263e+06	9.070867e+06	9.154302e+06	9.269612e+06	9.400145e+06
7	7	Argentina	ARG	Population, total	SP.POP.TOTL	2.061908e+07	2.095308e+07	2.128768e+07	2.162184e+07	2.195393e+07	2.228339e+07	2.260875e+07	2.293220e+07	2.326128e+07	2.360599e+07	2.397306e+07	2.436644e+07	2.478295e+07	2.521339e+07	2.564451e+07	2.606698e+07	2.647715e+07	2.687856e+07	2.727774e+07	2.768453e+07	2.810589e+07	2.854336e+07	2.899399e+07	2.945474e+07	2.992090e+07	3.038878e+07	3.085724e+07	3.132647e+07	3.179552e+07	3.226356e+07	3.272974e+07	3.319392e+07	3.365515e+07	3.411092e+07	3.455812e+07	3.499481e+07	3.541968e+07	3.583397e+07	3.624159e+07	3.664807e+07	3.705745e+07	3.747151e+07	3.788937e+07	3.830938e+07	3.872870e+07	3.914549e+07	3.955889e+07	3.997022e+07	4.038239e+07	4.079941e+07	4.122389e+07	4.165688e+07	4.209674e+07	4.253992e+07	4.298152e+07	4.341776e+07	4.384743e+07	4.427104e+07
8	8	Armenia	ARM	Population, total	SP.POP.TOTL	1.874120e+06	1.941491e+06	2.009526e+06	2.077575e+06	2.144998e+06	2.211316e+06	2.276031e+06	2.339124e+06	2.401140e+06	2.462925e+06	2.525065e+06	2.587706e+06	2.650484e+06	2.712781e+06	2.773747e+06	2.832757e+06	2.889579e+06	2.944379e+06	2.997411e+06	3.049105e+06	3.099751e+06	3.148092e+06	3.193686e+06	3.238594e+06	3.285595e+06	3.335935e+06	3.392256e+06	3.451942e+06	3.504651e+06	3.536469e+06	3.538165e+06	3.505251e+06	3.442810e+06	3.363098e+06	3.283660e+06	3.217342e+06	3.168215e+06	3.133086e+06	3.108684e+06	3.089017e+06	3.069588e+06	3.050655e+06	3.033897e+06	3.017806e+06	3.000612e+06	2.981259e+06	2.958500e+06	2.933056e+06	2.908220e+06	2.888584e+06	2.877311e+06	2.875581e+06	2.881922e+06	2.893509e+06	2.906220e+06	2.916950e+06	2.924816e+06	2.930450e+06
9	9	American Samoa	ASM	Population, total	SP.POP.TOTL	2.001300e+04	2.048600e+04	2.111700e+04	2.188200e+04	2.269800e+04	2.352000e+04	2.432100e+04	2.511600e+04	2.588500e+04	2.661400e+04	2.729200e+04	2.791600e+04	2.849200e+04	2.901400e+04	2.948800e+04	2.993200e+04	3.032100e+04	3.068900e+04	3.110200e+04	3.167300e+04	3.245700e+04	3.349300e+04	3.473800e+04	3.616000e+04	3.768800e+04	3.924100e+04	4.083700e+04	4.245000e+04	4.404700e+04	4.559300e+04	4.703800e+04	4.837500e+04	4.959300e+04	5.072000e+04	5.180300e+04	5.286800e+04	5.392900e+04	5.494100e+04	5.590100e+04	5.677000e+04	5.752100e+04	5.817500e+04	5.873100e+04	5.911700e+04	5.926400e+04	5.911800e+04	5.865000e+04	5.790300e+04	5.703000e+04	5.622700e+04	5.563700e+04	5.532000e+04	5.523000e+04	5.530700e+04	5.543700e+04	5.553700e+04	5.559900e+04	5.564100e+04
10	10	Antigua and Barbuda	ATG	Population, total	SP.POP.TOTL	5.533900e+04	5.614400e+04	5.714400e+04	5.829400e+04	5.952400e+04	6.078100e+04	6.205900e+04	6.336000e+04	6.465500e+04	6.591000e+04	6.709800e+04	6.818800e+04	6.917600e+04	7.006600e+04	7.087800e+04	7.160900e+04	7.228500e+04	7.287500e+04	7.332400e+04	7.352800e+04	7.344200e+04	7.306600e+04	7.244800e+04	7.163900e+04	7.072500e+04	6.978200e+04	6.880900e+04	6.784500e+04	6.705800e+04	6.662700e+04	6.669600e+04	6.730700e+04	6.842700e+04	6.993800e+04	7.171900e+04	7.361900e+04	7.562800e+04	7.773900e+04	7.985100e+04	8.183100e+04	8.358400e+04	8.505700e+04	8.626600e+04	8.729300e+04	8.825700e+04	8.925300e+04	9.030100e+04	9.138100e+04	9.247800e+04	9.358100e+04	9.466100e+04	9.571900e+04	9.677700e+04	9.782400e+04	9.887500e+04	9.992300e+04	1.009630e+05	1.020120e+05
11	11	Australia	AUS	Population, total	SP.POP.TOTL	1.027648e+07	1.048300e+07	1.074200e+07	1.095000e+07	1.116700e+07	1.138800e+07	1.165100e+07	1.179900e+07	1.200900e+07	1.226300e+07	1.250700e+07	1.293700e+07	1.317700e+07	1.338000e+07	1.372300e+07	1.389300e+07	1.403300e+07	1.419200e+07	1.435800e+07	1.451400e+07	1.469200e+07	1.492700e+07	1.517800e+07	1.536900e+07	1.554400e+07	1.575800e+07	1.601840e+07	1.626390e+07	1.653220e+07	1.681440e+07	1.706510e+07	1.728400e+07	1.749500e+07	1.766700e+07	1.785500e+07	1.807200e+07	1.831100e+07	1.851700e+07	1.871100e+07	1.892600e+07	1.915300e+07	1.941300e+07	1.965140e+07	1.989540e+07	2.012740e+07	2.039480e+07	2.069790e+07	2.082760e+07	2.124920e+07	2.169170e+07	2.203175e+07	2.234002e+07	2.274248e+07	2.314590e+07	2.350414e+07	2.385078e+07	2.421081e+07	2.459893e+07
12	12	Austria	AUT	Population, total	SP.POP.TOTL	7.047539e+06	7.086299e+06	7.129864e+06	7.175811e+06	7.223801e+06	7.270889e+06	7.322066e+06	7.376998e+06	7.415403e+06	7.441055e+06	7.467086e+06	7.500482e+06	7.544201e+06	7.586115e+06	7.599038e+06	7.578903e+06	7.565525e+06	7.568430e+06	7.562305e+06	7.549425e+06	7.549433e+06	7.568710e+06	7.574140e+06	7.561910e+06	7.561434e+06	7.564985e+06	7.569794e+06	7.574586e+06	7.585317e+06	7.619567e+06	7.677850e+06	7.754891e+06	7.840709e+06	7.905633e+06	7.936118e+06	7.948278e+06	7.959017e+06	7.968041e+06	7.976789e+06	7.992324e+06	8.011566e+06	8.042293e+06	8.081957e+06	8.121423e+06	8.171966e+06	8.227829e+06	8.268641e+06	8.295487e+06	8.321496e+06	8.343323e+06	8.363404e+06	8.391643e+06	8.429991e+06	8.479823e+06	8.546356e+06	8.642699e+06	8.736668e+06	8.809212e+06
13	13	Azerbaijan	AZE	Population, total	SP.POP.TOTL	3.895396e+06	4.030320e+06	4.171425e+06	4.315128e+06	4.456689e+06	4.592610e+06	4.721525e+06	4.843870e+06	4.960235e+06	5.071930e+06	5.180025e+06	5.284532e+06	5.385267e+06	5.483084e+06	5.579077e+06	5.674137e+06	5.768724e+06	5.863134e+06	5.957929e+06	6.053645e+06	6.150738e+06	6.249320e+06	6.349558e+06	6.452076e+06	6.557585e+06	6.666455e+06	6.778633e+06	6.893500e+06	7.010036e+06	7.126891e+06	7.159000e+06	7.271000e+06	7.382000e+06	7.495000e+06	7.597000e+06	7.685000e+06	7.763000e+06	7.838250e+06	7.913000e+06	7.982750e+06	8.048600e+06	8.111200e+06	8.171950e+06	8.234100e+06	8.306500e+06	8.391850e+06	8.484550e+06	8.581300e+06	8.763400e+06	8.947243e+06	9.054332e+06	9.173082e+06	9.295784e+06	9.416801e+06	9.535079e+06	9.649341e+06	9.757812e+06	9.862429e+06
14	14	Burundi	BDI	Population, total	SP.POP.TOTL	2.786106e+06	2.839666e+06	2.893669e+06	2.949926e+06	3.010859e+06	3.077876e+06	3.152723e+06	3.234023e+06	3.316233e+06	3.391753e+06	3.455606e+06	3.505391e+06	3.544047e+06	3.578490e+06	3.618585e+06	3.671494e+06	3.739659e+06	3.821194e+06	3.913768e+06	4.013310e+06	4.116817e+06	4.223195e+06	4.333386e+06	4.448728e+06	4.571292e+06	4.702066e+06	4.841565e+06	4.987736e+06	5.135956e+06	5.280024e+06	5.415415e+06	5.542048e+06	5.661139e+06	5.771398e+06	5.871607e+06	5.962058e+06	6.041112e+06	6.112097e+06	6.186352e+06	6.278940e+06	6.400706e+06	6.555829e+06	6.741569e+06	6.953113e+06	7.182451e+06	7.423289e+06	7.675338e+06	7.939573e+06	8.212264e+06	8.489031e+06	8.766930e+06	9.043508e+06	9.319710e+06	9.600186e+06	9.891790e+06	1.019927e+07	1.052412e+07	1.086424e+07
15	15	Belgium	BEL	Population, total	SP.POP.TOTL	9.153489e+06	9.183948e+06	9.220578e+06	9.289770e+06	9.378113e+06	9.463667e+06	9.527807e+06	9.580991e+06	9.618756e+06	9.646032e+06	9.655549e+06	9.673162e+06	9.711115e+06	9.741720e+06	9.772419e+06	9.800700e+06	9.818227e+06	9.830358e+06	9.839534e+06	9.848382e+06	9.859242e+06	9.858982e+06	9.856303e+06	9.855520e+06	9.855372e+06	9.858308e+06	9.861823e+06	9.870234e+06	9.901664e+06	9.937697e+06	9.967379e+06	1.000449e+07	1.004516e+07	1.008448e+07	1.011560e+07	1.013681e+07	1.015664e+07	1.018124e+07	1.020301e+07	1.022642e+07	1.025125e+07	1.028657e+07	1.033278e+07	1.037613e+07	1.042114e+07	1.047862e+07	1.054796e+07	1.062570e+07	1.070997e+07	1.079649e+07	1.089559e+07	1.104774e+07	1.112825e+07	1.118282e+07	1.120906e+07	1.127420e+07	1.133142e+07	1.137207e+07
16	16	Benin	BEN	Population, total	SP.POP.TOTL	2.431622e+06	2.465867e+06	2.502896e+06	2.542859e+06	2.585965e+06	2.632356e+06	2.682159e+06	2.735307e+06	2.791590e+06	2.850661e+06	2.912340e+06	2.976572e+06	3.043567e+06	3.113675e+06	3.187412e+06	3.265165e+06	3.347173e+06	3.433439e+06	3.523938e+06	3.618526e+06	3.717165e+06	3.820128e+06	3.927714e+06	4.039949e+06	4.156819e+06	4.278501e+06	4.404506e+06	4.535263e+06	4.672852e+06	4.820016e+06	4.978496e+06	5.149499e+06	5.331803e+06	5.521763e+06	5.714220e+06	5.905558e+06	6.094259e+06	6.281639e+06	6.470265e+06	6.664098e+06	6.865951e+06	7.076733e+06	7.295394e+06	7.520555e+06	7.750004e+06	7.982225e+06	8.216896e+06	8.454791e+06	8.696916e+06	8.944706e+06	9.199259e+06	9.460802e+06	9.729160e+06	1.000445e+07	1.028671e+07	1.057595e+07	1.087230e+07	1.117569e+07
17	17	Burkina Faso	BFA	Population, total	SP.POP.TOTL	4.829288e+06	4.894580e+06	4.960326e+06	5.027821e+06	5.098890e+06	5.174870e+06	5.256363e+06	5.343019e+06	5.434041e+06	5.528174e+06	5.624600e+06	5.723381e+06	5.825173e+06	5.930483e+06	6.040041e+06	6.154545e+06	6.274037e+06	6.398935e+06	6.530819e+06	6.671656e+06	6.822843e+06	6.985160e+06	7.158255e+06	7.340905e+06	7.531242e+06	7.727907e+06	7.930694e+06	8.140073e+06	8.356305e+06	8.579823e+06	8.811034e+06	9.050084e+06	9.297113e+06	9.552476e+06	9.816588e+06	1.008988e+07	1.037274e+07	1.066555e+07	1.096872e+07	1.128270e+07	1.160794e+07	1.194459e+07	1.229310e+07	1.265462e+07	1.303057e+07	1.342193e+07	1.382918e+07	1.425202e+07	1.468973e+07	1.514110e+07	1.560522e+07	1.608190e+07	1.657122e+07	1.707272e+07	1.758598e+07	1.811062e+07	1.864643e+07	1.919338e+07
18	18	Bangladesh	BGD	Population, total	SP.POP.TOTL	4.819975e+07	4.959280e+07	5.103014e+07	5.253242e+07	5.412910e+07	5.583404e+07	5.767299e+07	5.962067e+07	6.157947e+07	6.341739e+07	6.504777e+07	6.642474e+07	6.759747e+07	6.869118e+07	6.988442e+07	7.130592e+07	7.299914e+07	7.492590e+07	7.703385e+07	7.923678e+07	8.147086e+07	8.372127e+07	8.600733e+07	8.833824e+07	9.073236e+07	9.319986e+07	9.574243e+07	9.834381e+07	1.009753e+08	1.035992e+08	1.061886e+08	1.087274e+08	1.112219e+08	1.136951e+08	1.161823e+08	1.187069e+08	1.212696e+08	1.238546e+08	1.264480e+08	1.290297e+08	1.315812e+08	1.341072e+08	1.366007e+08	1.390190e+08	1.413075e+08	1.434311e+08	1.453680e+08	1.471392e+08	1.488058e+08	1.504547e+08	1.521491e+08	1.539119e+08	1.557271e+08	1.575713e+08	1.594053e+08	1.612009e+08	1.629516e+08	1.646698e+08
19	19	Bulgaria	BGR	Population, total	SP.POP.TOTL	7.867374e+06	7.943118e+06	8.012946e+06	8.078145e+06	8.144340e+06	8.204168e+06	8.258057e+06	8.310226e+06	8.369603e+06	8.434172e+06	8.489574e+06	8.536395e+06	8.576200e+06	8.620967e+06	8.678745e+06	8.720742e+06	8.758599e+06	8.804183e+06	8.814032e+06	8.825940e+06	8.861535e+06	8.891117e+06	8.917457e+06	8.939738e+06	8.960679e+06	8.960547e+06	8.958171e+06	8.971359e+06	8.981446e+06	8.876972e+06	8.718289e+06	8.632367e+06	8.540164e+06	8.472313e+06	8.443591e+06	8.406067e+06	8.362826e+06	8.312068e+06	8.256786e+06	8.210624e+06	8.170172e+06	8.009142e+06	7.837161e+06	7.775327e+06	7.716860e+06	7.658972e+06	7.601022e+06	7.545338e+06	7.492561e+06	7.444443e+06	7.395599e+06	7.348328e+06	7.305888e+06	7.265115e+06	7.223938e+06	7.177991e+06	7.127822e+06	7.075991e+06
20	20	Bahrain	BHR	Population, total	SP.POP.TOTL	1.624270e+05	1.678940e+05	1.731440e+05	1.781400e+05	1.828870e+05	1.874310e+05	1.917800e+05	1.960630e+05	2.006530e+05	2.060430e+05	2.126050e+05	2.203120e+05	2.291550e+05	2.395270e+05	2.519110e+05	2.665430e+05	2.837520e+05	3.031750e+05	3.234730e+05	3.427980e+05	3.598880e+05	3.741200e+05	3.859500e+05	3.964540e+05	4.072270e+05	4.194300e+05	4.334820e+05	4.489730e+05	4.652020e+05	4.810900e+05	4.959310e+05	5.097650e+05	5.230870e+05	5.362130e+05	5.495880e+05	5.636990e+05	5.786680e+05	5.949300e+05	6.137020e+05	6.365450e+05	6.646140e+05	6.975490e+05	7.351480e+05	7.787110e+05	8.298480e+05	8.891680e+05	9.584140e+05	1.035891e+06	1.114590e+06	1.185029e+06	1.240862e+06	1.278269e+06	1.300217e+06	1.315411e+06	1.336397e+06	1.371855e+06	1.425171e+06	1.492584e+06
21	21	Bahamas, The	BHS	Population, total	SP.POP.TOTL	1.095280e+05	1.151080e+05	1.210830e+05	1.273330e+05	1.336980e+05	1.400540e+05	1.463660e+05	1.526090e+05	1.586270e+05	1.642480e+05	1.693540e+05	1.738630e+05	1.778390e+05	1.814880e+05	1.850990e+05	1.888820e+05	1.929020e+05	1.971110e+05	2.015130e+05	2.060320e+05	2.106610e+05	2.153960e+05	2.202750e+05	2.251870e+05	2.300150e+05	2.346870e+05	2.391310e+05	2.433930e+05	2.475790e+05	2.518490e+05	2.563360e+05	2.611160e+05	2.661340e+05	2.711650e+05	2.758950e+05	2.801500e+05	2.837900e+05	2.869700e+05	2.900600e+05	2.935720e+05	2.978900e+05	3.031350e+05	3.091570e+05	3.157460e+05	3.225260e+05	3.292490e+05	3.358300e+05	3.423280e+05	3.486760e+05	3.548560e+05	3.608320e+05	3.665680e+05	3.720390e+05	3.772400e+05	3.821690e+05	3.868380e+05	3.912320e+05	3.953610e+05
22	22	Bosnia and Herzegovina	BIH	Population, total	SP.POP.TOTL	3.225668e+06	3.288602e+06	3.353226e+06	3.417574e+06	3.478995e+06	3.535640e+06	3.586634e+06	3.632669e+06	3.675452e+06	3.717466e+06	3.760527e+06	3.805285e+06	3.851151e+06	3.897255e+06	3.942223e+06	3.985103e+06	4.025265e+06	4.063191e+06	4.100350e+06	4.138819e+06	4.179855e+06	4.222511e+06	4.265310e+06	4.308106e+06	4.350746e+06	4.392130e+06	4.435504e+06	4.478519e+06	4.508056e+06	4.506653e+06	4.463422e+06	4.371603e+06	4.239154e+06	4.087999e+06	3.948816e+06	3.843712e+06	3.780378e+06	3.752431e+06	3.750485e+06	3.759118e+06	3.766706e+06	3.771284e+06	3.775807e+06	3.779247e+06	3.781287e+06	3.781530e+06	3.779468e+06	3.774000e+06	3.763599e+06	3.746561e+06	3.722084e+06	3.688865e+06	3.648200e+06	3.604999e+06	3.566002e+06	3.535961e+06	3.516816e+06	3.507017e+06
23	23	Belarus	BLR	Population, total	SP.POP.TOTL	8.198000e+06	8.271216e+06	8.351928e+06	8.437232e+06	8.524224e+06	8.610000e+06	8.696496e+06	8.785648e+06	8.874552e+06	8.960304e+06	9.040000e+06	9.115576e+06	9.188968e+06	9.257272e+06	9.317584e+06	9.367000e+06	9.411000e+06	9.463000e+06	9.525000e+06	9.584000e+06	9.643000e+06	9.710000e+06	9.776000e+06	9.843000e+06	9.910000e+06	9.975000e+06	1.004300e+07	1.011100e+07	1.014000e+07	1.017000e+07	1.018900e+07	1.019400e+07	1.021600e+07	1.023900e+07	1.022700e+07	1.019400e+07	1.016000e+07	1.011700e+07	1.006900e+07	1.002674e+07	9.979610e+06	9.928549e+06	9.865548e+06	9.796749e+06	9.730146e+06	9.663915e+06	9.604924e+06	9.560953e+06	9.527985e+06	9.506765e+06	9.490583e+06	9.473172e+06	9.464495e+06	9.465997e+06	9.474511e+06	9.489616e+06	9.501534e+06	9.507875e+06
24	24	Belize	BLZ	Population, total	SP.POP.TOTL	9.206400e+04	9.470300e+04	9.738400e+04	1.001640e+05	1.030690e+05	1.061190e+05	1.093470e+05	1.126920e+05	1.160610e+05	1.192610e+05	1.221820e+05	1.247930e+05	1.271500e+05	1.292940e+05	1.313070e+05	1.332600e+05	1.351470e+05	1.369890e+05	1.389650e+05	1.413050e+05	1.441550e+05	1.475660e+05	1.515000e+05	1.558220e+05	1.603470e+05	1.649210e+05	1.695680e+05	1.743200e+05	1.790280e+05	1.834690e+05	1.875520e+05	1.911260e+05	1.943170e+05	1.976160e+05	2.016740e+05	2.069630e+05	2.136760e+05	2.216060e+05	2.302840e+05	2.390260e+05	2.473150e+05	2.549840e+05	2.622060e+05	2.691300e+05	2.760890e+05	2.832770e+05	2.907470e+05	2.984070e+05	3.061650e+05	3.139290e+05	3.216080e+05	3.291920e+05	3.367010e+05	3.441810e+05	3.516940e+05	3.592880e+05	3.669540e+05	3.746810e+05
25	25	Bermuda	BMU	Population, total	SP.POP.TOTL	4.440000e+04	4.550000e+04	4.660000e+04	4.770000e+04	4.890000e+04	5.010000e+04	5.100000e+04	5.200000e+04	5.300000e+04	5.400000e+04	5.500000e+04	5.460000e+04	5.420000e+04	5.380000e+04	5.340000e+04	5.300000e+04	5.320000e+04	5.340000e+04	5.360000e+04	5.380000e+04	5.467000e+04	5.505000e+04	5.544900e+04	5.593000e+04	5.642300e+04	5.689800e+04	5.738200e+04	5.784900e+04	5.834700e+04	5.884100e+04	5.932600e+04	5.902100e+04	5.859500e+04	5.891000e+04	5.932000e+04	5.974600e+04	6.012900e+04	6.049700e+04	6.094300e+04	6.128500e+04	6.183300e+04	6.250400e+04	6.291200e+04	6.332500e+04	6.374000e+04	6.415400e+04	6.452300e+04	6.488800e+04	6.527300e+04	6.563600e+04	6.512400e+04	6.456400e+04	6.479800e+04	6.500100e+04	6.513900e+04	6.523900e+04	6.534100e+04	6.544100e+04
26	26	Bolivia	BOL	Population, total	SP.POP.TOTL	3.693449e+06	3.764813e+06	3.838097e+06	3.913395e+06	3.990857e+06	4.070590e+06	4.152668e+06	4.237125e+06	4.324064e+06	4.413590e+06	4.505778e+06	4.600591e+06	4.698083e+06	4.798509e+06	4.902168e+06	5.009257e+06	5.119833e+06	5.233677e+06	5.350322e+06	5.469123e+06	5.589575e+06	5.711599e+06	5.835182e+06	5.959960e+06	6.085496e+06	6.211550e+06	6.337893e+06	6.464732e+06	6.592787e+06	6.723046e+06	6.856244e+06	6.992521e+06	7.131707e+06	7.273825e+06	7.418861e+06	7.566714e+06	7.717443e+06	7.870855e+06	8.026254e+06	8.182712e+06	8.339512e+06	8.496375e+06	8.653345e+06	8.810420e+06	8.967741e+06	9.125409e+06	9.283334e+06	9.441444e+06	9.599855e+06	9.758748e+06	9.918242e+06	1.007834e+07	1.023900e+07	1.040026e+07	1.056216e+07	1.072470e+07	1.088788e+07	1.105160e+07
27	27	Brazil	BRA	Population, total	SP.POP.TOTL	7.220755e+07	7.435176e+07	7.657325e+07	7.885402e+07	8.116865e+07	8.349802e+07	8.583780e+07	8.819138e+07	9.055706e+07	9.293507e+07	9.532679e+07	9.772896e+07	1.001436e+08	1.025843e+08	1.050694e+08	1.076121e+08	1.102131e+08	1.128679e+08	1.155777e+08	1.183426e+08	1.211598e+08	1.240309e+08	1.269474e+08	1.298823e+08	1.328007e+08	1.356763e+08	1.384995e+08	1.412735e+08	1.440015e+08	1.466920e+08	1.493521e+08	1.519766e+08	1.545643e+08	1.571327e+08	1.597051e+08	1.622966e+08	1.649133e+08	1.675452e+08	1.701706e+08	1.727592e+08	1.752876e+08	1.777507e+08	1.801510e+08	1.824821e+08	1.847385e+08	1.869174e+08	1.890124e+08	1.910266e+08	1.929790e+08	1.948960e+08	1.967963e+08	1.986867e+08	2.005610e+08	2.024086e+08	2.042131e+08	2.059621e+08	2.076529e+08	2.092883e+08
28	28	Barbados	BRB	Population, total	SP.POP.TOTL	2.309390e+05	2.316780e+05	2.325860e+05	2.335870e+05	2.345470e+05	2.353740e+05	2.360440e+05	2.366210e+05	2.371990e+05	2.379130e+05	2.388480e+05	2.400350e+05	2.414410e+05	2.429760e+05	2.445390e+05	2.460340e+05	2.474440e+05	2.487840e+05	2.500320e+05	2.511770e+05	2.521940e+05	2.530800e+05	2.538410e+05	2.545180e+05	2.551930e+05	2.559240e+05	2.567360e+05	2.576110e+05	2.585270e+05	2.594580e+05	2.603740e+05	2.612750e+05	2.621840e+05	2.630890e+05	2.640150e+05	2.649590e+05	2.659420e+05	2.669450e+05	2.679500e+05	2.689220e+05	2.698470e+05	2.706850e+05	2.714780e+05	2.722580e+05	2.730910e+05	2.740090e+05	2.750390e+05	2.761500e+05	2.773190e+05	2.784700e+05	2.795690e+05	2.806010e+05	2.815850e+05	2.825090e+05	2.833850e+05	2.842170e+05	2.849960e+05	2.857190e+05
29	29	Brunei Darussalam	BRN	Population, total	SP.POP.TOTL	8.174500e+04	8.559600e+04	8.951600e+04	9.357600e+04	9.784800e+04	1.024250e+05	1.073160e+05	1.124940e+05	1.179500e+05	1.236530e+05	1.295830e+05	1.357260e+05	1.420730e+05	1.485600e+05	1.551090e+05	1.616710e+05	1.682240e+05	1.747730e+05	1.812570e+05	1.876560e+05	1.939490e+05	2.000850e+05	2.061280e+05	2.121360e+05	2.182270e+05	2.245120e+05	2.309720e+05	2.376220e+05	2.444580e+05	2.515140e+05	2.587850e+05	2.662740e+05	2.739630e+05	2.817510e+05	2.895250e+05	2.971920e+05	3.046990e+05	3.120380e+05	3.192220e+05	3.262890e+05	3.332410e+05	3.401170e+05	3.468670e+05	3.533890e+05	3.595230e+05	3.651580e+05	3.702500e+05	3.748640e+05	3.792520e+05	3.837720e+05	3.886620e+05	3.940130e+05	3.997480e+05	4.057160e+05	4.117040e+05	4.175420e+05	4.231960e+05	4.286970e+05
30	30	Bhutan	BTN	Population, total	SP.POP.TOTL	2.232880e+05	2.289180e+05	2.347060e+05	2.407780e+05	2.473250e+05	2.544640e+05	2.622440e+05	2.706220e+05	2.795150e+05	2.887740e+05	2.983010e+05	3.080530e+05	3.180450e+05	3.283120e+05	3.389430e+05	3.499820e+05	3.614550e+05	3.733240e+05	3.853840e+05	3.973900e+05	4.091720e+05	4.203800e+05	4.310500e+05	4.418470e+05	4.537200e+05	4.671780e+05	4.829520e+05	5.004370e+05	5.172730e+05	5.302570e+05	5.372800e+05	5.372840e+05	5.315250e+05	5.231170e+05	5.165030e+05	5.148770e+05	5.192820e+05	5.287540e+05	5.421550e+05	5.575430e+05	5.734160e+05	5.896000e+05	6.063990e+05	6.234340e+05	6.402820e+05	6.566390e+05	6.722280e+05	6.869580e+05	7.009500e+05	7.144580e+05	7.276410e+05	7.405100e+05	7.529670e+05	7.649610e+05	7.764480e+05	7.873860e+05	7.977650e+05	8.076100e+05
31	31	Botswana	BWA	Population, total	SP.POP.TOTL	5.245520e+05	5.372490e+05	5.508400e+05	5.653530e+05	5.807990e+05	5.971900e+05	6.146130e+05	6.331540e+05	6.528430e+05	6.736400e+05	6.955970e+05	7.186390e+05	7.428350e+05	7.685120e+05	7.960950e+05	8.258400e+05	8.578550e+05	8.919260e+05	9.275850e+05	9.641660e+05	1.001158e+06	1.038397e+06	1.075889e+06	1.113539e+06	1.151292e+06	1.189114e+06	1.226810e+06	1.264314e+06	1.301818e+06	1.339624e+06	1.377912e+06	1.416731e+06	1.455833e+06	1.494693e+06	1.532622e+06	1.569094e+06	1.604060e+06	1.637635e+06	1.669625e+06	1.699862e+06	1.728340e+06	1.754935e+06	1.779953e+06	1.804339e+06	1.829330e+06	1.855852e+06	1.884238e+06	1.914414e+06	1.946351e+06	1.979882e+06	2.014866e+06	2.051339e+06	2.089315e+06	2.128507e+06	2.168573e+06	2.209197e+06	2.250260e+06	2.291661e+06
32	32	Central African Republic	CAF	Population, total	SP.POP.TOTL	1.503508e+06	1.529227e+06	1.556661e+06	1.585763e+06	1.616516e+06	1.648833e+06	1.682885e+06	1.718603e+06	1.755344e+06	1.792220e+06	1.828709e+06	1.864598e+06	1.900317e+06	1.936841e+06	1.975521e+06	2.017372e+06	2.062405e+06	2.110457e+06	2.162249e+06	2.218575e+06	2.279821e+06	2.346797e+06	2.418844e+06	2.493135e+06	2.565803e+06	2.634232e+06	2.696982e+06	2.755244e+06	2.812244e+06	2.872668e+06	2.939780e+06	3.014624e+06	3.095807e+06	3.181222e+06	3.267670e+06	3.352767e+06	3.435821e+06	3.517309e+06	3.597385e+06	3.676508e+06	3.754986e+06	3.832203e+06	3.907612e+06	3.981665e+06	4.055036e+06	4.127910e+06	4.201758e+06	4.275800e+06	4.345386e+06	4.404230e+06	4.448525e+06	4.476153e+06	4.490416e+06	4.499653e+06	4.515392e+06	4.546100e+06	4.594621e+06	4.659080e+06
33	33	Canada	CAN	Population, total	SP.POP.TOTL	1.790901e+07	1.827100e+07	1.861400e+07	1.896400e+07	1.932500e+07	1.967800e+07	2.004800e+07	2.041200e+07	2.074400e+07	2.102800e+07	2.132400e+07	2.164554e+07	2.199363e+07	2.236941e+07	2.277409e+07	2.320900e+07	2.351800e+07	2.379600e+07	2.403600e+07	2.427700e+07	2.459300e+07	2.490000e+07	2.520200e+07	2.545600e+07	2.570200e+07	2.594200e+07	2.620400e+07	2.655000e+07	2.689500e+07	2.737900e+07	2.779100e+07	2.817168e+07	2.851960e+07	2.883341e+07	2.911191e+07	2.935400e+07	2.967190e+07	2.998720e+07	3.024790e+07	3.049920e+07	3.076970e+07	3.108190e+07	3.136200e+07	3.167600e+07	3.199500e+07	3.231200e+07	3.257050e+07	3.288793e+07	3.324577e+07	3.362857e+07	3.400527e+07	3.434278e+07	3.475054e+07	3.515237e+07	3.553535e+07	3.583251e+07	3.626460e+07	3.670808e+07
34	34	Central Europe and the Baltics	CEB	Population, total	SP.POP.TOTL	9.140158e+07	9.223712e+07	9.301489e+07	9.384575e+07	9.472260e+07	9.544706e+07	9.614864e+07	9.704359e+07	9.788239e+07	9.860214e+07	9.913330e+07	9.963898e+07	1.003636e+08	1.011205e+08	1.019463e+08	1.028625e+08	1.037701e+08	1.045893e+08	1.053043e+08	1.059248e+08	1.065649e+08	1.071880e+08	1.077708e+08	1.083269e+08	1.088532e+08	1.093603e+08	1.098471e+08	1.102967e+08	1.106885e+08	1.108014e+08	1.107458e+08	1.102904e+08	1.100056e+08	1.100815e+08	1.100196e+08	1.099132e+08	1.095631e+08	1.094591e+08	1.092072e+08	1.091024e+08	1.084055e+08	1.078004e+08	1.070976e+08	1.067608e+08	1.064661e+08	1.061738e+08	1.059013e+08	1.055045e+08	1.051267e+08	1.049244e+08	1.045438e+08	1.041740e+08	1.039353e+08	1.037137e+08	1.034962e+08	1.032578e+08	1.029943e+08	1.027271e+08
35	35	Switzerland	CHE	Population, total	SP.POP.TOTL	5.327827e+06	5.434294e+06	5.573815e+06	5.694247e+06	5.789228e+06	5.856472e+06	5.918002e+06	5.991785e+06	6.067714e+06	6.136387e+06	6.180877e+06	6.213399e+06	6.260956e+06	6.307347e+06	6.341405e+06	6.338632e+06	6.302504e+06	6.281174e+06	6.281738e+06	6.294365e+06	6.319408e+06	6.354074e+06	6.391309e+06	6.418773e+06	6.441865e+06	6.470365e+06	6.504124e+06	6.545106e+06	6.593386e+06	6.646912e+06	6.715519e+06	6.799978e+06	6.875364e+06	6.938265e+06	6.993795e+06	7.040687e+06	7.071850e+06	7.088906e+06	7.110001e+06	7.143991e+06	7.184250e+06	7.229854e+06	7.284753e+06	7.339001e+06	7.389625e+06	7.437115e+06	7.483934e+06	7.551117e+06	7.647675e+06	7.743831e+06	7.824909e+06	7.912398e+06	7.996861e+06	8.089346e+06	8.188649e+06	8.282396e+06	8.373338e+06	8.466017e+06
36	36	Channel Islands	CHI	Population, total	SP.POP.TOTL	1.094200e+05	1.103990e+05	1.114570e+05	1.125950e+05	1.137730e+05	1.149950e+05	1.162270e+05	1.174740e+05	1.187260e+05	1.199720e+05	1.211970e+05	1.224130e+05	1.236140e+05	1.247250e+05	1.256820e+05	1.264150e+05	1.269020e+05	1.271830e+05	1.273900e+05	1.276920e+05	1.282120e+05	1.289810e+05	1.299790e+05	1.311560e+05	1.324530e+05	1.338080e+05	1.352300e+05	1.367160e+05	1.381870e+05	1.395300e+05	1.406710e+05	1.415680e+05	1.422580e+05	1.428190e+05	1.433840e+05	1.440460e+05	1.448290e+05	1.457150e+05	1.466710e+05	1.476870e+05	1.487250e+05	1.497930e+05	1.509010e+05	1.520380e+05	1.531700e+05	1.542940e+05	1.554110e+05	1.565130e+05	1.575810e+05	1.586030e+05	1.595810e+05	1.604970e+05	1.613580e+05	1.621800e+05	1.629690e+05	1.637580e+05	1.645410e+05	1.653140e+05
37	37	Chile	CHL	Population, total	SP.POP.TOTL	7.716625e+06	7.890156e+06	8.067136e+06	8.247415e+06	8.430838e+06	8.617077e+06	8.806137e+06	8.997325e+06	9.188822e+06	9.378243e+06	9.563865e+06	9.745189e+06	9.922558e+06	1.009630e+07	1.026706e+07	1.043553e+07	1.060184e+07	1.076642e+07	1.093078e+07	1.109687e+07	1.126623e+07	1.143914e+07	1.161584e+07	1.179753e+07	1.198566e+07	1.218103e+07	1.238411e+07	1.259414e+07	1.280902e+07	1.302580e+07	1.324213e+07	1.345724e+07	1.367103e+07	1.388267e+07	1.409139e+07	1.429661e+07	1.449783e+07	1.469484e+07	1.488776e+07	1.507695e+07	1.526275e+07	1.544497e+07	1.562364e+07	1.579954e+07	1.597378e+07	1.614706e+07	1.631979e+07	1.649169e+07	1.666194e+07	1.682944e+07	1.699335e+07	1.715336e+07	1.730975e+07	1.746298e+07	1.761380e+07	1.776268e+07	1.790975e+07	1.805473e+07
38	38	China	CHN	Population, total	SP.POP.TOTL	6.670700e+08	6.603300e+08	6.657700e+08	6.823350e+08	6.983550e+08	7.151850e+08	7.354000e+08	7.545500e+08	7.745100e+08	7.960250e+08	8.183150e+08	8.411050e+08	8.620300e+08	8.819400e+08	9.003500e+08	9.163950e+08	9.306850e+08	9.434550e+08	9.561650e+08	9.690050e+08	9.812350e+08	9.938850e+08	1.008630e+09	1.023310e+09	1.036825e+09	1.051040e+09	1.066790e+09	1.084035e+09	1.101630e+09	1.118650e+09	1.135185e+09	1.150780e+09	1.164970e+09	1.178440e+09	1.191835e+09	1.204855e+09	1.217550e+09	1.230075e+09	1.241935e+09	1.252735e+09	1.262645e+09	1.271850e+09	1.280400e+09	1.288400e+09	1.296075e+09	1.303720e+09	1.311020e+09	1.317885e+09	1.324655e+09	1.331260e+09	1.337705e+09	1.344130e+09	1.350695e+09	1.357380e+09	1.364270e+09	1.371220e+09	1.378665e+09	1.386395e+09
39	39	Cote d'Ivoire	CIV	Population, total	SP.POP.TOTL	3.558988e+06	3.694205e+06	3.841071e+06	3.996941e+06	4.157965e+06	4.321791e+06	4.487204e+06	4.656353e+06	4.834279e+06	5.027971e+06	5.242395e+06	5.479338e+06	5.737281e+06	6.013862e+06	6.305287e+06	6.608609e+06	6.922982e+06	7.248828e+06	7.585914e+06	7.934279e+06	8.293675e+06	8.664057e+06	9.044473e+06	9.432731e+06	9.826055e+06	1.022256e+07	1.062027e+07	1.101965e+07	1.142426e+07	1.183924e+07	1.226775e+07	1.271001e+07	1.316302e+07	1.362273e+07	1.408361e+07	1.454082e+07	1.499525e+07	1.544599e+07	1.588455e+07	1.630023e+07	1.668656e+07	1.704015e+07	1.736652e+07	1.767936e+07	1.799774e+07	1.833630e+07	1.869944e+07	1.908594e+07	1.949799e+07	1.993637e+07	2.040133e+07	2.089531e+07	2.141860e+07	2.196631e+07	2.253135e+07	2.310847e+07	2.369592e+07	2.429475e+07

	Country Name	Country Code	Indicator Name	Indicator Code	1960	1961	1962	1963	1964	1965	1966	1967	1968	1969	1970	1971	1972	1973	1974	1975	1976	1977	1978	1979	1980	1981	1982	1983	1984	1985	1986	1987	1988	1989	1990	1991	1992	1993	1994	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017
33	Canada	CAN	Population, total	SP.POP.TOTL	17909009.0	18271000.0	18614000.0	18964000.0	19325000.0	19678000.0	20048000.0	20412000.0	20744000.0	21028000.0	21324000.0	21645535.0	21993631.0	22369408.0	22774087.0	23209000.0	23518000.0	23796000.0	24036000.0	24277000.0	24593000.0	24900000.0	25202000.0	25456000.0	25702000.0	25942000.0	26204000.0	26550000.0	26895000.0	27379000.0	27791000.0	28171682.0	28519597.0	28833410.0	29111906.0	29354000.0	29671900.0	29987200.0	30247900.0	30499200.0	30769700.0	31081900.0	31362000.0	31676000.0	31995000.0	32312000.0	32570505.0	32887928.0	33245773.0	33628571.0	34005274.0	34342780.0	34750545.0	35152370.0	35535348.0	35832513.0	36264604.0	36708083.0
152	Mexico	MEX	Population, total	SP.POP.TOTL	38174112.0	39394126.0	40649588.0	41939880.0	43264272.0	44623043.0	46011038.0	47429812.0	48894019.0	50423481.0	52029861.0	53718724.0	55478151.0	57280587.0	59088193.0	60872399.0	62623763.0	64345884.0	66039488.0	67709689.0	69360871.0	70992195.0	72602533.0	74196548.0	75780605.0	77360707.0	78934125.0	80503052.0	82083919.0	83697891.0	85357874.0	87071512.0	88828310.0	90600453.0	92349147.0	94045579.0	95687452.0	97281739.0	98821456.0	100300579.0	101719673.0	103067068.0	104355608.0	105640453.0	106995583.0	108472228.0	110092378.0	111836346.0	113661809.0	115505228.0	117318941.0	119090017.0	120828307.0	122535969.0	124221600.0	125890949.0	127540423.0	129163276.0
249	United States	USA	Population, total	SP.POP.TOTL	180671000.0	183691000.0	186538000.0	189242000.0	191889000.0	194303000.0	196560000.0	198712000.0	200706000.0	202677000.0	205052000.0	207661000.0	209896000.0	211909000.0	213854000.0	215973000.0	218035000.0	220239000.0	222585000.0	225055000.0	227225000.0	229466000.0	231664000.0	233792000.0	235825000.0	237924000.0	240133000.0	242289000.0	244499000.0	246819000.0	249623000.0	252981000.0	256514000.0	259919000.0	263126000.0	266278000.0	269394000.0	272657000.0	275854000.0	279040000.0	282162411.0	284968955.0	287625193.0	290107933.0	292805298.0	295516599.0	298379912.0	301231207.0	304093966.0	306771529.0	309338421.0	311644280.0	313993272.0	316234505.0	318622525.0	321039839.0	323405935.0	325719178.0

	totalamt	year	Social Protection
0	0	2018.0	0
1	200000000	2018.0	0
2	58330000	2018.0	0
3	20000000	2018.0	0
4	100000000	2018.0	1

Data Pipelines: ETL vs ELT¶

ETL¶

ELT¶

Outline of the Lesson¶

World Bank Data¶

How to Tackle the Exercises¶

What You Will do in the Next Section¶

Summary of the data file types you'll work with¶

CSV files¶

JSON¶

XML¶

SQL databases¶

Text Files¶

Extracting Data from the Web¶

Goal of the ETL Lesson¶

Extracting data from a csv file¶

Part 1 projects_data.csv¶

Part 2 population_data.csv¶

Conclusion¶

Extract from JSON and XML¶

Extract JSON and JSON Exercise¶

Other Ways to Read in JSON¶

Extract XML¶

How to read and navigate XML¶

XML Exercise (Challenge)¶

Conclusion¶

Extract Data from SQL Databases¶

Pandas and sqlite3¶

Demo: SQLite3 and Pandas¶

Demo: SQLAlchemy and Pandas¶

Exercise¶

APIs¶

Before there were APIs¶

Using APIs¶

Example Indicators API¶

Exercise Indicators API¶

Combining Data¶

Exercise 1¶

Exercise 2 (Challenge)¶

Cleaning Data¶

Part 1 - Explore the Data¶

Part 2 - Use the Pycountry library¶

Part 3 - Making a Manual Mapping¶

Part 5 - Make a 'Country Code' Column¶

Conclusion¶

Data Types¶

Exercise 1¶

Exercise 2¶

Conclusion¶

Parsing Dates¶

Part 1 - Practice Parsing Dates¶

Part 2 - Create new columns¶

Part 3 (Challenge)¶

Conclusion¶

Encodings¶

Conclusion¶

Imputing Data¶

Exercise - Part 1¶

Excercise - Part 2¶

Exercise - Part 3¶

Conclusion¶

Duplicate Data¶

Exercise 1¶

Exercise 2 (challenge)¶

Conclusion¶

Dummy Variables Exercise¶

Exercise 1¶

Exercise 2¶

replace() method¶

regular expressions¶

Exercise 3¶

Exercise 4 (Challenge)¶

Conclusion¶